What is integrative thinking, and how can it help with collaboration?
The method called Integrative Thinking, coined by Jennifer Riel and Roger Martin in their book Creating Great Choices , involves taking different ideas and examining the problem they are trying to solve, with the end goal of opening up to new thinking and innovation.
People tend to think about difficult decision making in the workplace as a matter of choosing from a preselected list of options, usually handed down to them from management.
👉 Learn more about digital collaboration from facilitation experts like Dan in the MURAL Community!
What is Integrated Thinking?
Integrated Thinking reframes decision making by moving it away from the process of settling on a compromise, towards actively breaking a challenge or opportunity down into its core elements and reassembling them into a new option that doesn’t compromise. Integrated Thinking allows two opposing solutions to be combined into a stronger option through a carefully designed and structured process.
Here we are going to break down the process into simple steps. Our goal is to provide enough of an overview so you can lead your team through the Integrative Thinking framework in your ideation process.
Integrated Thinking can help your team
Exploring, remixing, and testing new methods of working encourages people to have the confidence to take risks and think expansively to yield more interesting outcomes. By adding tools and frameworks such as Integrative Thinking, you can close the gap between wanting to solve the problem and the practical action needed to make it happen.
How to run an Integrative Thinking workshop
You can break the over process into two main phases with four big steps. The first two steps are designed to seek understanding and sense-making of the challenge. The second two are used to generate new models.
Step 1: Articulate the models
Understand the problem and options, holding space to clarify the two different and/or opposing models/ideas you wish to explore more deeply.
Define the problem
If you haven’t already, create a Problem Statement. Get the sentiment, it is important that your team feels it needs solving. A problem statement should clearly articulate:
- Who is impacted (you can start quite broadly at this stage)?
- What is the problem, opportunity, need, insight, or challenge?
- Where is this happening (for context)?
- Why is it worth solving? The big “why,” then the why from a business and stakeholder point of view in regard to value creation.
Related: How to identify the right problem to solve
Identify potential answers to the problem
Here we invite your team to brainstorm and come up with as many ideas as possible. Whether from experience, competitors, or original concepts inspired by knowledge and insights. As a group, select the two extreme and/or opposing models or ideas (if more abstract). By transforming a general problem into a two-sided dilemma you can explore the fundamental tension between them and discover the best information to generate possibilities.
To make a decision we suggest using dot voting. For those unfamiliar with dot voting, it involves sticky dots and people silently voting on what they feel has the most potential. With platforms like Mural this is part of the functionality.
🚀 Make the brainstorming process easier with these effective brainstorming techniques .
Convey the opposing ideas clearly
Take the time to condense each idea down to a simple, jargon free, model. Think of these ideas being so clear that an external party would be able to enter your meeting and quickly understand what they are. These should be simple enough that people understand the essence of each model, the fundamental components .
Step 2: Examine the models
Once we have articulated the models the next step is to link this to the people who are impacted most. Creating a stakeholder analysis is a critical component of any human-centered design process . Not only does this keep the group honest. It also takes the conversation away from the organizational and sometimes biased view of value creation. Ideally you would have customer insights or data to refer. Or you can use a simple Stakeholder Analysis 2×2 .
At this stage it is ok to be scrappy. Once you have some assumptions to test you can add more fidelity to your personas . You just want to get started. We suggest selecting a maximum of two people/cohorts. Your goal here is to focus on why each stakeholder might value the outcomes of each model. To do this as a group create a top five pro-pro list. To lead this discussion ask your group to consider:
- How each model works for each person, what is its Job To Be Done ?
- How do the stakeholders feel about the existing solutions, how do they cause joy and frustration?
- How do related outcomes impact stakeholders?
Once you have these stakeholder insights you will be ready to focus on how the models are similar and different, or where is the tension between them.
Determining what you most value from a model is a highly subjective task. Different people will value different aspects, this can help you determine benefits of each model because explaining why you value something can prompt deeper thinking and internal discovery.
Related: Learn how to map your assumptions in Mural's guide to assumptions mapping .
Step 3: Explore the possibilities
After the models are defined and examined, the third stage signals a (big) shift to ideation. Here we use a structured process to remix and redesign with the goal of generating a new option(s). There are three approaches to achieve this. None are exclusive to Integrative Thinking. However, the power of the Integrative Thinking process is getting people to a mindset where they feel comfortable and confident to ideate with their team:
The Hidden Gem
We create a new model using only a building block from each model. Here we refer to the insights previously captured to take a component from each of the models.
The Double Down
Where we take one model and add only one benefit from the other.
Hot tip: Rather than take a view of which is the most favorable, we encourage you to consider which components from each of the models you’d be loath to give up!
Here we put the models together in a new way so that each model can be applied in whole to specific parts of the problem , e.g. how could they co-exist as part of a process?
We suggest running through all of the approaches (The Hidden Gem, The Double Down, The Decomposition) until you have generated a number of possibilities. The point is to ask the question and see what comes of it as you never know where an idea could lead.
Step 4: Assess the prototypes
The final stage is to test your prototype solution in order to discard or improve them. The simplest testing method is to share the ideas clearly with the people you included in the process — or if that fails —someone external to the group workshop. The goal is to learn if the new approach is a better experience, more effective solution, etc. Tests should be created to help you learn from the prototype (approach). Ensure you document all insights and present back to the team before deciding on the next steps.
There are three main outcomes you can expect from testing:
- Win: You have evidence to move from design into risk assessment and/or design implementation.
- A flawed success: You know you are on the right track, iterate and try again.
- An efficient failure: You get to fail fast and move on.
Note: This article originally appeared on Morespaceforlight
About the authors
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How to master the seven-step problem-solving process
In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.
Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.
Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].
Charles and Hugo, welcome to the podcast. Thank you for being here.
Hugo Sarrazin: Our pleasure.
Charles Conn: It’s terrific to be here.
Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?
Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”
You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”
I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.
I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.
Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.
Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.
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Simon London: So this is a concise problem statement.
Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.
Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.
How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.
Hugo Sarrazin: Yeah.
Charles Conn: And in the wrong direction.
Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?
Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.
What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.
Simon London: What’s a good example of a logic tree on a sort of ratable problem?
Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.
If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.
When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.
Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.
Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.
People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.
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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?
Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.
Simon London: Not going to have a lot of depth to it.
Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.
Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.
Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.
Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.
Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.
Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.
Simon London: Right. Right.
Hugo Sarrazin: So it’s the same thing in problem solving.
Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.
Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?
Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.
Simon London: Would you agree with that?
Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.
You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.
Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?
Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.
Simon London: Step six. You’ve done your analysis.
Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”
Simon London: But, again, these final steps are about motivating people to action, right?
Charles Conn: Yeah.
Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.
Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.
Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.
Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.
Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?
Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.
You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.
Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.
Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”
Hugo Sarrazin: Every step of the process.
Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?
Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.
Simon London: Problem definition, but out in the world.
Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.
Simon London: So, Charles, are these complements or are these alternatives?
Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.
Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?
Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.
The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.
Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.
Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.
Hugo Sarrazin: Absolutely.
Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.
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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.
Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.
Charles Conn: It was a pleasure to be here, Simon.
Hugo Sarrazin: It was a pleasure. Thank you.
Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.
Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.
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7 Problem-Solving Skills That Can Help You Be a More Successful Manager
Discover what problem-solving is, and why it's important for managers. Understand the steps of the process and learn about seven problem-solving skills.
1Managers oversee the day-to-day operations of a particular department, and sometimes a whole company, using their problem-solving skills regularly. Managers with good problem-solving skills can help ensure companies run smoothly and prosper.
If you're a current manager or are striving to become one, read this guide to discover what problem-solving skills are and why it's important for managers to have them. Learn the steps of the problem-solving process, and explore seven skills that can help make problem-solving easier and more effective.
What is problem-solving?
Problem-solving is both an ability and a process. As an ability, problem-solving can aid in resolving issues faced in different environments like home, school, abroad, and social situations, among others. As a process, problem-solving involves a series of steps for finding solutions to questions or concerns that arise throughout life.
The importance of problem-solving for managers
Managers deal with problems regularly, whether supervising a staff of two or 100. When people solve problems quickly and effectively, workplaces can benefit in a number of ways. These include:
Increased job fulfillment
Satisfied clients or customers
Better cooperation and cohesion
Improved environments for employees and customers
7 skills that make problem-solving easier
Companies depend on managers who can solve problems adeptly. Although problem-solving is a skill in its own right, a subset of seven skills can help make the process of problem-solving easier. These include analysis, communication, emotional intelligence, resilience, creativity, adaptability, and teamwork.
As a manager , you'll solve each problem by assessing the situation first. Then, you’ll use analytical skills to distinguish between ineffective and effective solutions.
Effective communication plays a significant role in problem-solving, particularly when others are involved. Some skills that can help enhance communication at work include active listening, speaking with an even tone and volume, and supporting verbal information with written communication.
3. Emotional intelligence
Emotional intelligence is the ability to recognize and manage emotions in any situation. People with emotional intelligence usually solve problems calmly and systematically, which often yields better results.
Emotional intelligence and resilience are closely related traits. Resiliency is the ability to cope with and bounce back quickly from difficult situations. Those who possess resilience are often capable of accurately interpreting people and situations, which can be incredibly advantageous when difficulties arise.
When brainstorming solutions to problems, creativity can help you to think outside the box. Problem-solving strategies can be enhanced with the application of creative techniques. You can use creativity to:
Approach problems from different angles
Improve your problem-solving process
Spark creativity in your employees and peers
Adaptability is the capacity to adjust to change. When a particular solution to an issue doesn't work, an adaptable person can revisit the concern to think up another one without getting frustrated.
Finding a solution to a problem regularly involves working in a team. Good teamwork requires being comfortable working with others and collaborating with them, which can result in better problem-solving overall.
Steps of the problem-solving process
Effective problem-solving involves five essential steps. One way to remember them is through the IDEAL model created in 1984 by psychology professors John D. Bransford and Barry S. Stein [ 1 ]. The steps to solving problems in this model include: identifying that there is a problem, defining the goals you hope to achieve, exploring potential solutions, choosing a solution and acting on it, and looking at (or evaluating) the outcome.
1. Identify that there is a problem and root out its cause.
To solve a problem, you must first admit that one exists to then find its root cause. Finding the cause of the problem may involve asking questions like:
Can the problem be solved?
How big of a problem is it?
Why do I think the problem is occurring?
What are some things I know about the situation?
What are some things I don't know about the situation?
Are there any people who contributed to the problem?
Are there materials or processes that contributed to the problem?
Are there any patterns I can identify?
2. Define the goals you hope to achieve.
Every problem is different. The goals you hope to achieve when problem-solving depend on the scope of the problem. Some examples of goals you might set include:
Gather as much factual information as possible.
Brainstorm many different strategies to come up with the best one.
Be flexible when considering other viewpoints.
Articulate clearly and encourage questions, so everyone involved is on the same page.
Be open to other strategies if the chosen strategy doesn't work.
Stay positive throughout the process.
3. Explore potential solutions.
Once you've defined the goals you hope to achieve when problem-solving , it's time to start the process. This involves steps that often include fact-finding, brainstorming, prioritizing solutions, and assessing the cost of top solutions in terms of time, labor, and money.
4. Choose a solution and act on it.
Evaluate the pros and cons of each potential solution, and choose the one most likely to solve the problem within your given budget, abilities, and resources. Once you choose a solution, it's important to make a commitment and see it through. Draw up a plan of action for implementation, and share it with all involved parties clearly and effectively, both verbally and in writing. Make sure everyone understands their role for a successful conclusion.
5. Look at (or evaluate) the outcome.
Evaluation offers insights into your current situation and future problem-solving. When evaluating the outcome, ask yourself questions like:
Did the solution work?
Will this solution work for other problems?
Were there any changes you would have made?
Would another solution have worked better?
As a current or future manager looking to build your problem-solving skills, it is often helpful to take a professional course. Consider Improving Communication Skills offered by the University of Pennsylvania on Coursera. You'll learn how to boost your ability to persuade, ask questions, negotiate, apologize, and more.
You might also consider taking Emotional Intelligence: Cultivating Immensely Human Interactions , offered by the University of Michigan on Coursera. You'll explore the interpersonal and intrapersonal skills common to people with emotional intelligence, and you'll learn how emotional intelligence is connected to team success and leadership.
Tennessee Tech. “ The Ideal Problem Solver (2nd ed.) , https://www.tntech.edu/cat/pdf/useful_links/idealproblemsolver.pdf.” Accessed December 6, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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Why Problem-Solving Skills Are Essential for Leaders in Any Industry
- 17 Jan 2023
Any organization offering a product or service is in the business of solving problems.
Whether providing medical care to address health issues or quick convenience to those hungry for dinner, a business’s purpose is to satisfy customer needs .
In addition to solving customers’ problems, you’ll undoubtedly encounter challenges within your organization as it evolves to meet customer needs. You’re likely to experience growing pains in the form of missed targets, unattained goals, and team disagreements.
Yet, the ubiquity of problems doesn’t have to be discouraging; with the right frameworks and tools, you can build the skills to solve consumers' and your organization’s most challenging issues.
Here’s a primer on problem-solving in business, why it’s important, the skills you need, and how to build them.
Access your free e-book today.
What Is Problem-Solving in Business?
Problem-solving is the process of systematically removing barriers that prevent you or others from reaching goals.
Your business removes obstacles in customers’ lives through its products or services, just as you can remove obstacles that keep your team from achieving business goals.
Design thinking , as described by Harvard Business School Dean Srikant Datar in the online course Design Thinking and Innovation , is a human-centered , solutions-based approach to problem-solving and innovation. Originally created for product design, design thinking’s use case has evolved . It’s now used to solve internal business problems, too.
The design thinking process has four stages :
- Clarify: Clarify a problem through research and feedback from those impacted.
- Ideate: Armed with new insights, generate as many solutions as possible.
- Develop: Combine and cull your ideas into a short list of viable, feasible, and desirable options before building prototypes (if making physical products) and creating a plan of action (if solving an intangible problem).
- Implement: Execute the strongest idea, ensuring clear communication with all stakeholders about its potential value and deliberate reasoning.
Using this framework, you can generate innovative ideas that wouldn’t have surfaced otherwise.
Another, less structured approach to challenges is creative problem-solving , which employs a series of exercises to explore open-ended solutions and develop new perspectives. This is especially useful when a problem’s root cause has yet to be defined.
You can use creative problem-solving tools in design thinking’s “ideate” stage, which include:
- Brainstorming: Instruct everyone to develop as many ideas as possible in an allotted time frame without passing judgment.
- Divergent thinking exercises: Rather than arriving at the same conclusion (convergent thinking), instruct everyone to come up with a unique idea for a given prompt (divergent thinking). This type of exercise helps avoid the tendency to agree with others’ ideas without considering alternatives.
- Alternate worlds: Ask your team to consider how various personas would manage the problem. For instance, how would a pilot approach it? What about a young child? What about a seasoned engineer?
It can be tempting to fall back on how problems have been solved before, especially if they worked well. However, if you’re striving for innovation, relying on existing systems can stunt your company’s growth.
Related: How to Be a More Creative Problem-Solver at Work: 8 Tips
Why Is Problem-Solving Important for Leaders?
While obstacles’ specifics vary between industries, strong problem-solving skills are crucial for leaders in any field.
Whether building a new product or dealing with internal issues, you’re bound to come up against challenges. Having frameworks and tools at your disposal when they arise can turn issues into opportunities.
As a leader, it’s rarely your responsibility to solve a problem single-handedly, so it’s crucial to know how to empower employees to work together to find the best solution.
Your job is to guide them through each step of the framework and set the parameters and prompts within which they can be creative. Then, you can develop a list of ideas together, test the best ones, and implement the chosen solution.
Related: 5 Design Thinking Skills for Business Professionals
4 Problem-Solving Skills All Leaders Need
1. problem framing.
One key skill for any leader is framing problems in a way that makes sense for their organization. Problem framing is defined in Design Thinking and Innovation as determining the scope, context, and perspective of the problem you’re trying to solve.
“Before you begin to generate solutions for your problem, you must always think hard about how you’re going to frame that problem,” Datar says in the course.
For instance, imagine you work for a company that sells children’s sneakers, and sales have plummeted. When framing the problem, consider:
- What is the children’s sneaker market like right now?
- Should we improve the quality of our sneakers?
- Should we assess all children’s footwear?
- Is this a marketing issue for children’s sneakers specifically?
- Is this a bigger issue that impacts how we should market or produce all footwear?
While there’s no one right way to frame a problem, how you do can impact the solutions you generate. It’s imperative to accurately frame problems to align with organizational priorities and ensure your team generates useful ideas for your firm.
To solve a problem, you need to empathize with those impacted by it. Empathy is the ability to understand others’ emotions and experiences. While many believe empathy is a fixed trait, it’s a skill you can strengthen through practice.
When confronted with a problem, consider whom it impacts. Returning to the children’s sneaker example, think of who’s affected:
- Your organization’s employees, because sales are down
- The customers who typically buy your sneakers
- The children who typically wear your sneakers
Empathy is required to get to the problem’s root and consider each group’s perspective. Assuming someone’s perspective often isn’t accurate, so the best way to get that information is by collecting user feedback.
For instance, if you asked customers who typically buy your children’s sneakers why they’ve stopped, they could say, “A new brand of children’s sneakers came onto the market that have soles with more traction. I want my child to be as safe as possible, so I bought those instead.”
When someone shares their feelings and experiences, you have an opportunity to empathize with them. This can yield solutions to their problem that directly address its root and shows you care. In this case, you may design a new line of children’s sneakers with extremely grippy soles for added safety, knowing that’s what your customers care most about.
Related: 3 Effective Methods for Assessing Customer Needs
3. Breaking Cognitive Fixedness
Cognitive fixedness is a state of mind in which you examine situations through the lens of past experiences. This locks you into one mindset rather than allowing you to consider alternative possibilities.
For instance, your cognitive fixedness may make you think rubber is the only material for sneaker treads. What else could you use? Is there a grippier alternative you haven’t considered?
Problem-solving is all about overcoming cognitive fixedness. You not only need to foster this skill in yourself but among your team.
4. Creating a Psychologically Safe Environment
As a leader, it’s your job to create an environment conducive to problem-solving. In a psychologically safe environment, all team members feel comfortable bringing ideas to the table, which are likely influenced by their personal opinions and experiences.
If employees are penalized for “bad” ideas or chastised for questioning long-held procedures and systems, innovation has no place to take root.
By employing the design thinking framework and creative problem-solving exercises, you can foster a setting in which your team feels comfortable sharing ideas and new, innovative solutions can grow.
How to Build Problem-Solving Skills
The most obvious answer to how to build your problem-solving skills is perhaps the most intimidating: You must practice.
Again and again, you’ll encounter challenges, use creative problem-solving tools and design thinking frameworks, and assess results to learn what to do differently next time.
While most of your practice will occur within your organization, you can learn in a lower-stakes setting by taking an online course, such as Design Thinking and Innovation . Datar guides you through each tool and framework, presenting real-world business examples to help you envision how you would approach the same types of problems in your organization.
Are you interested in uncovering innovative solutions for your organization’s business problems? Explore Design Thinking and Innovation —one of our online entrepreneurship and innovation courses —to learn how to leverage proven frameworks and tools to solve challenges. Not sure which course is right for you? Download our free flowchart .
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Overview of the Problem-Solving Mental Process
Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.
- Identify the Problem
- Define the Problem
- Form a Strategy
- Organize Information
- Allocate Resources
- Monitor Progress
- Evaluate the Results
Frequently Asked Questions
Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.
The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.
It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.
In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.
The following steps include developing strategies and organizing knowledge.
1. Identifying the Problem
While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.
Some strategies that you might use to figure out the source of a problem include :
- Asking questions about the problem
- Breaking the problem down into smaller pieces
- Looking at the problem from different perspectives
- Conducting research to figure out what relationships exist between different variables
2. Defining the Problem
After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address
At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.
3. Forming a Strategy
After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.
The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.
- Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
- Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.
Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.
4. Organizing Information
Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.
When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.
5. Allocating Resources
Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.
If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.
At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.
6. Monitoring Progress
After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.
It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.
Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .
7. Evaluating the Results
After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.
Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.
A Word From Verywell
It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.
Get Advice From The Verywell Mind Podcast
Hosted by therapist Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares how you can stop dwelling in a negative mindset.
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You can become a better problem solving by:
- Practicing brainstorming and coming up with multiple potential solutions to problems
- Being open-minded and considering all possible options before making a decision
- Breaking down problems into smaller, more manageable pieces
- Asking for help when needed
- Researching different problem-solving techniques and trying out new ones
- Learning from mistakes and using them as opportunities to grow
It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.
Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.
If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.
Davidson JE, Sternberg RJ, editors. The Psychology of Problem Solving . Cambridge University Press; 2003. doi:10.1017/CBO9780511615771
Sarathy V. Real world problem-solving . Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
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How to improve your problem solving skills and build effective problem solving strategies
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Effective problem solving is all about using the right process and following a plan tailored to the issue at hand. Recognizing your team or organization has an issue isn’t enough to come up with effective problem solving strategies.
To truly understand a problem and develop appropriate solutions, you will want to follow a solid process, follow the necessary problem solving steps, and bring all of your problem solving skills to the table.
We’ll first guide you through the seven step problem solving process you and your team can use to effectively solve complex business challenges. We’ll also look at what problem solving strategies you can employ with your team when looking for a way to approach the process. We’ll then discuss the problem solving skills you need to be more effective at solving problems, complete with an activity from the SessionLab library you can use to develop that skill in your team.
Let’s get to it!
What is a problem solving process?
- What are the problem solving steps I need to follow?
Problem solving strategies
What skills do i need to be an effective problem solver, how can i improve my problem solving skills.
Solving problems is like baking a cake. You can go straight into the kitchen without a recipe or the right ingredients and do your best, but the end result is unlikely to be very tasty!
Using a process to bake a cake allows you to use the best ingredients without waste, collect the right tools, account for allergies, decide whether it is a birthday or wedding cake, and then bake efficiently and on time. The result is a better cake that is fit for purpose, tastes better and has created less mess in the kitchen. Also, it should have chocolate sprinkles. Having a step by step process to solve organizational problems allows you to go through each stage methodically and ensure you are trying to solve the right problems and select the most appropriate, effective solutions.
What are the problem solving steps I need to follow?
All problem solving processes go through a number of steps in order to move from identifying a problem to resolving it.
Depending on your problem solving model and who you ask, there can be anything between four and nine problem solving steps you should follow in order to find the right solution. Whatever framework you and your group use, there are some key items that should be addressed in order to have an effective process.
We’ve looked at problem solving processes from sources such as the American Society for Quality and their four step approach , and Mediate ‘s six step process. By reflecting on those and our own problem solving processes, we’ve come up with a sequence of seven problem solving steps we feel best covers everything you need in order to effectively solve problems.
1. Problem identification
The first stage of any problem solving process is to identify the problem or problems you might want to solve. Effective problem solving strategies always begin by allowing a group scope to articulate what they believe the problem to be and then coming to some consensus over which problem they approach first. Problem solving activities used at this stage often have a focus on creating frank, open discussion so that potential problems can be brought to the surface.
2. Problem analysis
Though this step is not a million miles from problem identification, problem analysis deserves to be considered separately. It can often be an overlooked part of the process and is instrumental when it comes to developing effective solutions.
The process of problem analysis means ensuring that the problem you are seeking to solve is the right problem . As part of this stage, you may look deeper and try to find the root cause of a specific problem at a team or organizational level.
Remember that problem solving strategies should not only be focused on putting out fires in the short term but developing long term solutions that deal with the root cause of organizational challenges.
Whatever your approach, analyzing a problem is crucial in being able to select an appropriate solution and the problem solving skills deployed in this stage are beneficial for the rest of the process and ensuring the solutions you create are fit for purpose.
3. Solution generation
Once your group has nailed down the particulars of the problem you wish to solve, you want to encourage a free flow of ideas connecting to solving that problem. This can take the form of problem solving games that encourage creative thinking or problem solving activities designed to produce working prototypes of possible solutions.
The key to ensuring the success of this stage of the problem solving process is to encourage quick, creative thinking and create an open space where all ideas are considered. The best solutions can come from unlikely places and by using problem solving techniques that celebrate invention, you might come up with solution gold.
4. Solution development
No solution is likely to be perfect right out of the gate. It’s important to discuss and develop the solutions your group has come up with over the course of following the previous problem solving steps in order to arrive at the best possible solution. Problem solving games used in this stage involve lots of critical thinking, measuring potential effort and impact, and looking at possible solutions analytically.
During this stage, you will often ask your team to iterate and improve upon your frontrunning solutions and develop them further. Remember that problem solving strategies always benefit from a multitude of voices and opinions, and not to let ego get involved when it comes to choosing which solutions to develop and take further.
Finding the best solution is the goal of all problem solving workshops and here is the place to ensure that your solution is well thought out, sufficiently robust and fit for purpose.
5. Decision making
Nearly there! Once your group has reached consensus and selected a solution that applies to the problem at hand you have some decisions to make. You will want to work on allocating ownership of the project, figure out who will do what, how the success of the solution will be measured and decide the next course of action.
The decision making stage is a part of the problem solving process that can get missed or taken as for granted. Fail to properly allocate roles and plan out how a solution will actually be implemented and it less likely to be successful in solving the problem.
Have clear accountabilities, actions, timeframes, and follow-ups. Make these decisions and set clear next-steps in the problem solving workshop so that everyone is aligned and you can move forward effectively as a group.
Ensuring that you plan for the roll-out of a solution is one of the most important problem solving steps. Without adequate planning or oversight, it can prove impossible to measure success or iterate further if the problem was not solved.
6. Solution implementation
This is what we were waiting for! All problem solving strategies have the end goal of implementing a solution and solving a problem in mind.
Remember that in order for any solution to be successful, you need to help your group through all of the previous problem solving steps thoughtfully. Only then can you ensure that you are solving the right problem but also that you have developed the correct solution and can then successfully implement and measure the impact of that solution.
Project management and communication skills are key here – your solution may need to adjust when out in the wild or you might discover new challenges along the way.
7. Solution evaluation
So you and your team developed a great solution to a problem and have a gut feeling its been solved. Work done, right? Wrong. All problem solving strategies benefit from evaluation, consideration, and feedback. You might find that the solution does not work for everyone, might create new problems, or is potentially so successful that you will want to roll it out to larger teams or as part of other initiatives.
None of that is possible without taking the time to evaluate the success of the solution you developed in your problem solving model and adjust if necessary.
Remember that the problem solving process is often iterative and it can be common to not solve complex issues on the first try. Even when this is the case, you and your team will have generated learning that will be important for future problem solving workshops or in other parts of the organization.
It’s worth underlining how important record keeping is throughout the problem solving process. If a solution didn’t work, you need to have the data and records to see why that was the case. If you go back to the drawing board, notes from the previous workshop can help save time. Data and insight is invaluable at every stage of the problem solving process and this one is no different.
Problem solving workshops made easy
Problem solving strategies are methods of approaching and facilitating the process of problem-solving with a set of techniques , actions, and processes. Different strategies are more effective if you are trying to solve broad problems such as achieving higher growth versus more focused problems like, how do we improve our customer onboarding process?
Broadly, the problem solving steps outlined above should be included in any problem solving strategy though choosing where to focus your time and what approaches should be taken is where they begin to differ. You might find that some strategies ask for the problem identification to be done prior to the session or that everything happens in the course of a one day workshop.
The key similarity is that all good problem solving strategies are structured and designed. Four hours of open discussion is never going to be as productive as a four-hour workshop designed to lead a group through a problem solving process.
Good problem solving strategies are tailored to the team, organization and problem you will be attempting to solve. Here are some example problem solving strategies you can learn from or use to get started.
Use a workshop to lead a team through a group process
Often, the first step to solving problems or organizational challenges is bringing a group together effectively. Most teams have the tools, knowledge, and expertise necessary to solve their challenges – they just need some guidance in how to use leverage those skills and a structure and format that allows people to focus their energies.
Facilitated workshops are one of the most effective ways of solving problems of any scale. By designing and planning your workshop carefully, you can tailor the approach and scope to best fit the needs of your team and organization.
Problem solving workshop
- Creating a bespoke, tailored process
- Tackling problems of any size
- Building in-house workshop ability and encouraging their use
Workshops are an effective strategy for solving problems. By using tried and test facilitation techniques and methods, you can design and deliver a workshop that is perfectly suited to the unique variables of your organization. You may only have the capacity for a half-day workshop and so need a problem solving process to match.
By using our session planner tool and importing methods from our library of 700+ facilitation techniques, you can create the right problem solving workshop for your team. It might be that you want to encourage creative thinking or look at things from a new angle to unblock your groups approach to problem solving. By tailoring your workshop design to the purpose, you can help ensure great results.
One of the main benefits of a workshop is the structured approach to problem solving. Not only does this mean that the workshop itself will be successful, but many of the methods and techniques will help your team improve their working processes outside of the workshop.
We believe that workshops are one of the best tools you can use to improve the way your team works together. Start with a problem solving workshop and then see what team building, culture or design workshops can do for your organization!
Run a design sprint
- aligning large, multi-discipline teams
- quickly designing and testing solutions
- tackling large, complex organizational challenges and breaking them down into smaller tasks
By using design thinking principles and methods, a design sprint is a great way of identifying, prioritizing and prototyping solutions to long term challenges that can help solve major organizational problems with quick action and measurable results.
Some familiarity with design thinking is useful, though not integral, and this strategy can really help a team align if there is some discussion around which problems should be approached first.
The stage-based structure of the design sprint is also very useful for teams new to design thinking. The inspiration phase, where you look to competitors that have solved your problem, and the rapid prototyping and testing phases are great for introducing new concepts that will benefit a team in all their future work.
It can be common for teams to look inward for solutions and so looking to the market for solutions you can iterate on can be very productive. Instilling an agile prototyping and testing mindset can also be great when helping teams move forwards – generating and testing solutions quickly can help save time in the long run and is also pretty exciting!
Break problems down into smaller issues
Organizational challenges and problems are often complicated and large scale in nature. Sometimes, trying to resolve such an issue in one swoop is simply unachievable or overwhelming. Try breaking down such problems into smaller issues that you can work on step by step. You may not be able to solve the problem of churning customers off the bat, but you can work with your team to identify smaller effort but high impact elements and work on those first.
This problem solving strategy can help a team generate momentum, prioritize and get some easy wins. It’s also a great strategy to employ with teams who are just beginning to learn how to approach the problem solving process. If you want some insight into a way to employ this strategy, we recommend looking at our design sprint template below!
Use guiding frameworks or try new methodologies
Some problems are best solved by introducing a major shift in perspective or by using new methodologies that encourage your team to think differently.
Props and tools such as Methodkit , which uses a card-based toolkit for facilitation, or Lego Serious Play can be great ways to engage your team and find an inclusive, democratic problem solving strategy. Remember that play and creativity are great tools for achieving change and whatever the challenge, engaging your participants can be very effective where other strategies may have failed.
LEGO Serious Play
- Improving core problem solving skills
- Thinking outside of the box
- Encouraging creative solutions
LEGO Serious Play is a problem solving methodology designed to get participants thinking differently by using 3D models and kinesthetic learning styles. By physically building LEGO models based on questions and exercises, participants are encouraged to think outside of the box and create their own responses.
Collaborate LEGO Serious Play exercises are also used to encourage communication and build problem solving skills in a group. By using this problem solving process, you can often help different kinds of learners and personality types contribute and unblock organizational problems with creative thinking.
Problem solving strategies like LEGO Serious Play are super effective at helping a team solve more skills-based problems such as communication between teams or a lack of creative thinking. Some problems are not suited to LEGO Serious Play and require a different problem solving strategy.
Card Decks and Method Kits
- New facilitators or non-facilitators
- Approaching difficult subjects with a simple, creative framework
- Engaging those with varied learning styles
Card decks and method kids are great tools for those new to facilitation or for whom facilitation is not the primary role. Card decks such as the emotional culture deck can be used for complete workshops and in many cases, can be used right out of the box. Methodkit has a variety of kits designed for scenarios ranging from personal development through to personas and global challenges so you can find the right deck for your particular needs.
Having an easy to use framework that encourages creativity or a new approach can take some of the friction or planning difficulties out of the workshop process and energize a team in any setting. Simplicity is the key with these methods. By ensuring everyone on your team can get involved and engage with the process as quickly as possible can really contribute to the success of your problem solving strategy.
Source external advice
Looking to peers, experts and external facilitators can be a great way of approaching the problem solving process. Your team may not have the necessary expertise, insights of experience to tackle some issues, or you might simply benefit from a fresh perspective. Some problems may require bringing together an entire team, and coaching managers or team members individually might be the right approach. Remember that not all problems are best resolved in the same manner.
If you’re a solo entrepreneur, peer groups, coaches and mentors can also be invaluable at not only solving specific business problems, but in providing a support network for resolving future challenges. One great approach is to join a Mastermind Group and link up with like-minded individuals and all grow together. Remember that however you approach the sourcing of external advice, do so thoughtfully, respectfully and honestly. Reciprocate where you can and prepare to be surprised by just how kind and helpful your peers can be!
- Solo entrepreneurs or small teams with low capacity
- Peer learning and gaining outside expertise
- Getting multiple external points of view quickly
Problem solving in large organizations with lots of skilled team members is one thing, but how about if you work for yourself or in a very small team without the capacity to get the most from a design sprint or LEGO Serious Play session?
A mastermind group – sometimes known as a peer advisory board – is where a group of people come together to support one another in their own goals, challenges, and businesses. Each participant comes to the group with their own purpose and the other members of the group will help them create solutions, brainstorm ideas, and support one another.
Mastermind groups are very effective in creating an energized, supportive atmosphere that can deliver meaningful results. Learning from peers from outside of your organization or industry can really help unlock new ways of thinking and drive growth. Access to the experience and skills of your peers can be invaluable in helping fill the gaps in your own ability, particularly in young companies.
A mastermind group is a great solution for solo entrepreneurs, small teams, or for organizations that feel that external expertise or fresh perspectives will be beneficial for them. It is worth noting that Mastermind groups are often only as good as the participants and what they can bring to the group. Participants need to be committed, engaged and understand how to work in this context.
Coaching and mentoring
- Focused learning and development
- Filling skills gaps
- Working on a range of challenges over time
Receiving advice from a business coach or building a mentor/mentee relationship can be an effective way of resolving certain challenges. The one-to-one format of most coaching and mentor relationships can really help solve the challenges those individuals are having and benefit the organization as a result.
A great mentor can be invaluable when it comes to spotting potential problems before they arise and coming to understand a mentee very well has a host of other business benefits. You might run an internal mentorship program to help develop your team’s problem solving skills and strategies or as part of a large learning and development program. External coaches can also be an important part of your problem solving strategy, filling skills gaps for your management team or helping with specific business issues.
Now we’ve explored the problem solving process and the steps you will want to go through in order to have an effective session, let’s look at the skills you and your team need to be more effective problem solvers.
Problem solving skills are highly sought after, whatever industry or team you work in. Organizations are keen to employ people who are able to approach problems thoughtfully and find strong, realistic solutions. Whether you are a facilitator , a team leader or a developer, being an effective problem solver is a skill you’ll want to develop.
Problem solving skills form a whole suite of techniques and approaches that an individual uses to not only identify problems but to discuss them productively before then developing appropriate solutions.
Here are some of the most important problem solving skills everyone from executives to junior staff members should learn. We’ve also included an activity or exercise from the SessionLab library that can help you and your team develop that skill.
If you’re running a workshop or training session to try and improve problem solving skills in your team, try using these methods to supercharge your process!
Active listening is one of the most important skills anyone who works with people can possess. In short, active listening is a technique used to not only better understand what is being said by an individual, but also to be more aware of the underlying message the speaker is trying to convey. When it comes to problem solving, active listening is integral for understanding the position of every participant and to clarify the challenges, ideas and solutions they bring to the table.
Some active listening skills include:
- Paying complete attention to the speaker.
- Removing distractions.
- Avoid interruption.
- Taking the time to fully understand before preparing a rebuttal.
- Responding respectfully and appropriately.
- Demonstrate attentiveness and positivity with an open posture, making eye contact with the speaker, smiling and nodding if appropriate. Show that you are listening and encourage them to continue.
- Be aware of and respectful of feelings. Judge the situation and respond appropriately. You can disagree without being disrespectful.
- Observe body language.
- Paraphrase what was said in your own words, either mentally or verbally.
- Remain neutral.
- Reflect and take a moment before responding.
- Ask deeper questions based on what is said and clarify points where necessary.
Active Listening #hyperisland #skills #active listening #remote-friendly This activity supports participants to reflect on a question and generate their own solutions using simple principles of active listening and peer coaching. It’s an excellent introduction to active listening but can also be used with groups that are already familiar with it. Participants work in groups of three and take turns being: “the subject”, the listener, and the observer.
All problem solving models require strong analytical skills, particularly during the beginning of the process and when it comes to analyzing how solutions have performed.
Analytical skills are primarily focused on performing an effective analysis by collecting, studying and parsing data related to a problem or opportunity.
It often involves spotting patterns, being able to see things from different perspectives and using observable facts and data to make suggestions or produce insight.
Analytical skills are also important at every stage of the problem solving process and by having these skills, you can ensure that any ideas or solutions you create or backed up analytically and have been sufficiently thought out.
Nine Whys #innovation #issue analysis #liberating structures With breathtaking simplicity, you can rapidly clarify for individuals and a group what is essentially important in their work. You can quickly reveal when a compelling purpose is missing in a gathering and avoid moving forward without clarity. When a group discovers an unambiguous shared purpose, more freedom and more responsibility are unleashed. You have laid the foundation for spreading and scaling innovations with fidelity.
Trying to solve problems on your own is difficult. Being able to collaborate effectively, with a free exchange of ideas, to delegate and be a productive member of a team is hugely important to all problem solving strategies.
Remember that whatever your role, collaboration is integral, and in a problem solving process, you are all working together to find the best solution for everyone.
Marshmallow challenge with debriefing #teamwork #team #leadership #collaboration In eighteen minutes, teams must build the tallest free-standing structure out of 20 sticks of spaghetti, one yard of tape, one yard of string, and one marshmallow. The marshmallow needs to be on top. The Marshmallow Challenge was developed by Tom Wujec, who has done the activity with hundreds of groups around the world. Visit the Marshmallow Challenge website for more information. This version has an extra debriefing question added with sample questions focusing on roles within the team.
Being an effective communicator means being empathetic, clear and succinct, asking the right questions, and demonstrating active listening skills throughout any discussion or meeting.
In a problem solving setting, you need to communicate well in order to progress through each stage of the process effectively. As a team leader, it may also fall to you to facilitate communication between parties who may not see eye to eye. Effective communication also means helping others to express themselves and be heard in a group.
Bus Trip #feedback #communication #appreciation #closing #thiagi #team This is one of my favourite feedback games. I use Bus Trip at the end of a training session or a meeting, and I use it all the time. The game creates a massive amount of energy with lots of smiles, laughs, and sometimes even a teardrop or two.
Creative problem solving skills can be some of the best tools in your arsenal. Thinking creatively, being able to generate lots of ideas and come up with out of the box solutions is useful at every step of the process.
The kinds of problems you will likely discuss in a problem solving workshop are often difficult to solve, and by approaching things in a fresh, creative manner, you can often create more innovative solutions.
Having practical creative skills is also a boon when it comes to problem solving. If you can help create quality design sketches and prototypes in record time, it can help bring a team to alignment more quickly or provide a base for further iteration.
The paper clip method #sharing #creativity #warm up #idea generation #brainstorming The power of brainstorming. A training for project leaders, creativity training, and to catalyse getting new solutions.
Critical thinking is one of the fundamental problem solving skills you’ll want to develop when working on developing solutions. Critical thinking is the ability to analyze, rationalize and evaluate while being aware of personal bias, outlying factors and remaining open-minded.
Defining and analyzing problems without deploying critical thinking skills can mean you and your team go down the wrong path. Developing solutions to complex issues requires critical thinking too – ensuring your team considers all possibilities and rationally evaluating them.
Agreement-Certainty Matrix #issue analysis #liberating structures #problem solving You can help individuals or groups avoid the frequent mistake of trying to solve a problem with methods that are not adapted to the nature of their challenge. The combination of two questions makes it possible to easily sort challenges into four categories: simple, complicated, complex , and chaotic . A problem is simple when it can be solved reliably with practices that are easy to duplicate. It is complicated when experts are required to devise a sophisticated solution that will yield the desired results predictably. A problem is complex when there are several valid ways to proceed but outcomes are not predictable in detail. Chaotic is when the context is too turbulent to identify a path forward. A loose analogy may be used to describe these differences: simple is like following a recipe, complicated like sending a rocket to the moon, complex like raising a child, and chaotic is like the game “Pin the Tail on the Donkey.” The Liberating Structures Matching Matrix in Chapter 5 can be used as the first step to clarify the nature of a challenge and avoid the mismatches between problems and solutions that are frequently at the root of chronic, recurring problems.
Though it shares lots of space with general analytical skills, data analysis skills are something you want to cultivate in their own right in order to be an effective problem solver.
Being good at data analysis doesn’t just mean being able to find insights from data, but also selecting the appropriate data for a given issue, interpreting it effectively and knowing how to model and present that data. Depending on the problem at hand, it might also include a working knowledge of specific data analysis tools and procedures.
Having a solid grasp of data analysis techniques is useful if you’re leading a problem solving workshop but if you’re not an expert, don’t worry. Bring people into the group who has this skill set and help your team be more effective as a result.
All problems need a solution and all solutions require that someone make the decision to implement them. Without strong decision making skills, teams can become bogged down in discussion and less effective as a result.
Making decisions is a key part of the problem solving process. It’s important to remember that decision making is not restricted to the leadership team. Every staff member makes decisions every day and developing these skills ensures that your team is able to solve problems at any scale. Remember that making decisions does not mean leaping to the first solution but weighing up the options and coming to an informed, well thought out solution to any given problem that works for the whole team.
Lightning Decision Jam (LDJ) #action #decision making #problem solving #issue analysis #innovation #design #remote-friendly The problem with anything that requires creative thinking is that it’s easy to get lost—lose focus and fall into the trap of having useless, open-ended, unstructured discussions. Here’s the most effective solution I’ve found: Replace all open, unstructured discussion with a clear process. What to use this exercise for: Anything which requires a group of people to make decisions, solve problems or discuss challenges. It’s always good to frame an LDJ session with a broad topic, here are some examples: The conversion flow of our checkout Our internal design process How we organise events Keeping up with our competition Improving sales flow
Most complex organizational problems require multiple people to be involved in delivering the solution. Ensuring that the team and organization can depend on you to take the necessary actions and communicate where necessary is key to ensuring problems are solved effectively.
Being dependable also means working to deadlines and to brief. It is often a matter of creating trust in a team so that everyone can depend on one another to complete the agreed actions in the agreed time frame so that the team can move forward together. Being undependable can create problems of friction and can limit the effectiveness of your solutions so be sure to bear this in mind throughout a project.
Team Purpose & Culture #team #hyperisland #culture #remote-friendly This is an essential process designed to help teams define their purpose (why they exist) and their culture (how they work together to achieve that purpose). Defining these two things will help any team to be more focused and aligned. With support of tangible examples from other companies, the team members work as individuals and a group to codify the way they work together. The goal is a visual manifestation of both the purpose and culture that can be put up in the team’s work space.
Emotional intelligence is an important skill for any successful team member, whether communicating internally or with clients or users. In the problem solving process, emotional intelligence means being attuned to how people are feeling and thinking, communicating effectively and being self-aware of what you bring to a room.
There are often differences of opinion when working through problem solving processes, and it can be easy to let things become impassioned or combative. Developing your emotional intelligence means being empathetic to your colleagues and managing your own emotions throughout the problem and solution process. Be kind, be thoughtful and put your points across care and attention.
Being emotionally intelligent is a skill for life and by deploying it at work, you can not only work efficiently but empathetically. Check out the emotional culture workshop template for more!
As we’ve clarified in our facilitation skills post, facilitation is the art of leading people through processes towards agreed-upon objectives in a manner that encourages participation, ownership, and creativity by all those involved. While facilitation is a set of interrelated skills in itself, the broad definition of facilitation can be invaluable when it comes to problem solving. Leading a team through a problem solving process is made more effective if you improve and utilize facilitation skills – whether you’re a manager, team leader or external stakeholder.
The Six Thinking Hats #creative thinking #meeting facilitation #problem solving #issue resolution #idea generation #conflict resolution The Six Thinking Hats are used by individuals and groups to separate out conflicting styles of thinking. They enable and encourage a group of people to think constructively together in exploring and implementing change, rather than using argument to fight over who is right and who is wrong.
Being flexible is a vital skill when it comes to problem solving. This does not mean immediately bowing to pressure or changing your opinion quickly: instead, being flexible is all about seeing things from new perspectives, receiving new information and factoring it into your thought process.
Flexibility is also important when it comes to rolling out solutions. It might be that other organizational projects have greater priority or require the same resources as your chosen solution. Being flexible means understanding needs and challenges across the team and being open to shifting or arranging your own schedule as necessary. Again, this does not mean immediately making way for other projects. It’s about articulating your own needs, understanding the needs of others and being able to come to a meaningful compromise.
The Creativity Dice #creativity #problem solving #thiagi #issue analysis Too much linear thinking is hazardous to creative problem solving. To be creative, you should approach the problem (or the opportunity) from different points of view. You should leave a thought hanging in mid-air and move to another. This skipping around prevents premature closure and lets your brain incubate one line of thought while you consciously pursue another.
Working in any group can lead to unconscious elements of groupthink or situations in which you may not wish to be entirely honest. Disagreeing with the opinions of the executive team or wishing to save the feelings of a coworker can be tricky to navigate, but being honest is absolutely vital when to comes to developing effective solutions and ensuring your voice is heard.
Remember that being honest does not mean being brutally candid. You can deliver your honest feedback and opinions thoughtfully and without creating friction by using other skills such as emotional intelligence.
Explore your Values #hyperisland #skills #values #remote-friendly Your Values is an exercise for participants to explore what their most important values are. It’s done in an intuitive and rapid way to encourage participants to follow their intuitive feeling rather than over-thinking and finding the “correct” values. It is a good exercise to use to initiate reflection and dialogue around personal values.
The problem solving process is multi-faceted and requires different approaches at certain points of the process. Taking initiative to bring problems to the attention of the team, collect data or lead the solution creating process is always valuable. You might even roadtest your own small scale solutions or brainstorm before a session. Taking initiative is particularly effective if you have good deal of knowledge in that area or have ownership of a particular project and want to get things kickstarted.
That said, be sure to remember to honor the process and work in service of the team. If you are asked to own one part of the problem solving process and you don’t complete that task because your initiative leads you to work on something else, that’s not an effective method of solving business challenges.
15% Solutions #action #liberating structures #remote-friendly You can reveal the actions, however small, that everyone can do immediately. At a minimum, these will create momentum, and that may make a BIG difference. 15% Solutions show that there is no reason to wait around, feel powerless, or fearful. They help people pick it up a level. They get individuals and the group to focus on what is within their discretion instead of what they cannot change. With a very simple question, you can flip the conversation to what can be done and find solutions to big problems that are often distributed widely in places not known in advance. Shifting a few grains of sand may trigger a landslide and change the whole landscape.
A particularly useful problem solving skill for product owners or managers is the ability to remain impartial throughout much of the process. In practice, this means treating all points of view and ideas brought forward in a meeting equally and ensuring that your own areas of interest or ownership are not favored over others.
There may be a stage in the process where a decision maker has to weigh the cost and ROI of possible solutions against the company roadmap though even then, ensuring that the decision made is based on merit and not personal opinion.
Empathy map #frame insights #create #design #issue analysis An empathy map is a tool to help a design team to empathize with the people they are designing for. You can make an empathy map for a group of people or for a persona. To be used after doing personas when more insights are needed.
Being a good leader means getting a team aligned, energized and focused around a common goal. In the problem solving process, strong leadership helps ensure that the process is efficient, that any conflicts are resolved and that a team is managed in the direction of success.
It’s common for managers or executives to assume this role in a problem solving workshop, though it’s important that the leader maintains impartiality and does not bulldoze the group in a particular direction. Remember that good leadership means working in service of the purpose and team and ensuring the workshop is a safe space for employees of any level to contribute. Take a look at our leadership games and activities post for more exercises and methods to help improve leadership in your organization.
Leadership Pizza #leadership #team #remote-friendly This leadership development activity offers a self-assessment framework for people to first identify what skills, attributes and attitudes they find important for effective leadership, and then assess their own development and initiate goal setting.
In the context of problem solving, mediation is important in keeping a team engaged, happy and free of conflict. When leading or facilitating a problem solving workshop, you are likely to run into differences of opinion. Depending on the nature of the problem, certain issues may be brought up that are emotive in nature.
Being an effective mediator means helping those people on either side of such a divide are heard, listen to one another and encouraged to find common ground and a resolution. Mediating skills are useful for leaders and managers in many situations and the problem solving process is no different.
Conflict Responses #hyperisland #team #issue resolution A workshop for a team to reflect on past conflicts, and use them to generate guidelines for effective conflict handling. The workshop uses the Thomas-Killman model of conflict responses to frame a reflective discussion. Use it to open up a discussion around conflict with a team.
Solving organizational problems is much more effective when following a process or problem solving model. Planning skills are vital in order to structure, deliver and follow-through on a problem solving workshop and ensure your solutions are intelligently deployed.
Planning skills include the ability to organize tasks and a team, plan and design the process and take into account any potential challenges. Taking the time to plan carefully can save time and frustration later in the process and is valuable for ensuring a team is positioned for success.
3 Action Steps #hyperisland #action #remote-friendly This is a small-scale strategic planning session that helps groups and individuals to take action toward a desired change. It is often used at the end of a workshop or programme. The group discusses and agrees on a vision, then creates some action steps that will lead them towards that vision. The scope of the challenge is also defined, through discussion of the helpful and harmful factors influencing the group.
As organisations grow, the scale and variation of problems they face multiplies. Your team or is likely to face numerous challenges in different areas and so having the skills to analyze and prioritize becomes very important, particularly for those in leadership roles.
A thorough problem solving process is likely to deliver multiple solutions and you may have several different problems you wish to solve simultaneously. Prioritization is the ability to measure the importance, value, and effectiveness of those possible solutions and choose which to enact and in what order. The process of prioritization is integral in ensuring the biggest challenges are addressed with the most impactful solutions.
Impact and Effort Matrix #gamestorming #decision making #action #remote-friendly In this decision-making exercise, possible actions are mapped based on two factors: effort required to implement and potential impact. Categorizing ideas along these lines is a useful technique in decision making, as it obliges contributors to balance and evaluate suggested actions before committing to them.
Some problem solving skills are utilized in a workshop or ideation phases, while others come in useful when it comes to decision making. Overseeing an entire problem solving process and ensuring its success requires strong project management skills.
While project management incorporates many of the other skills listed here, it is important to note the distinction of considering all of the factors of a project and managing them successfully. Being able to negotiate with stakeholders, manage tasks, time and people, consider costs and ROI, and tie everything together is massively helpful when going through the problem solving process.
Working out meaningful solutions to organizational challenges is only one part of the process. Thoughtfully documenting and keeping records of each problem solving step for future consultation is important in ensuring efficiency and meaningful change.
For example, some problems may be lower priority than others but can be revisited in the future. If the team has ideated on solutions and found some are not up to the task, record those so you can rule them out and avoiding repeating work. Keeping records of the process also helps you improve and refine your problem solving model next time around!
Personal Kanban #gamestorming #action #agile #project planning Personal Kanban is a tool for organizing your work to be more efficient and productive. It is based on agile methods and principles.
Conducting research to support both the identification of problems and the development of appropriate solutions is important for an effective process. Knowing where to go to collect research, how to conduct research efficiently, and identifying pieces of research are relevant are all things a good researcher can do well.
In larger groups, not everyone has to demonstrate this ability in order for a problem solving workshop to be effective. That said, having people with research skills involved in the process, particularly if they have existing area knowledge, can help ensure the solutions that are developed with data that supports their intention. Remember that being able to deliver the results of research efficiently and in a way the team can easily understand is also important. The best data in the world is only as effective as how it is delivered and interpreted.
Customer experience map #ideation #concepts #research #design #issue analysis #remote-friendly Customer experience mapping is a method of documenting and visualizing the experience a customer has as they use the product or service. It also maps out their responses to their experiences. To be used when there is a solution (even in a conceptual stage) that can be analyzed.
Managing risk is an often overlooked part of the problem solving process. Solutions are often developed with the intention of reducing exposure to risk or solving issues that create risk but sometimes, great solutions are more experimental in nature and as such, deploying them needs to be carefully considered.
Managing risk means acknowledging that there may be risks associated with more out of the box solutions or trying new things, but that this must be measured against the possible benefits and other organizational factors.
Be informed, get the right data and stakeholders in the room and you can appropriately factor risk into your decision making process.
Decisions, Decisions… #communication #decision making #thiagi #action #issue analysis When it comes to decision-making, why are some of us more prone to take risks while others are risk-averse? One explanation might be the way the decision and options were presented. This exercise, based on Kahneman and Tversky’s classic study , illustrates how the framing effect influences our judgement and our ability to make decisions . The participants are divided into two groups. Both groups are presented with the same problem and two alternative programs for solving them. The two programs both have the same consequences but are presented differently. The debriefing discussion examines how the framing of the program impacted the participant’s decision.
No single person is as good at problem solving as a team. Building an effective team and helping them come together around a common purpose is one of the most important problem solving skills, doubly so for leaders. By bringing a team together and helping them work efficiently, you pave the way for team ownership of a problem and the development of effective solutions.
In a problem solving workshop, it can be tempting to jump right into the deep end, though taking the time to break the ice, energize the team and align them with a game or exercise will pay off over the course of the day.
Remember that you will likely go through the problem solving process multiple times over an organization’s lifespan and building a strong team culture will make future problem solving more effective. It’s also great to work with people you know, trust and have fun with. Working on team building in and out of the problem solving process is a hallmark of successful teams that can work together to solve business problems.
9 Dimensions Team Building Activity #ice breaker #teambuilding #team #remote-friendly 9 Dimensions is a powerful activity designed to build relationships and trust among team members. There are 2 variations of this icebreaker. The first version is for teams who want to get to know each other better. The second version is for teams who want to explore how they are working together as a team.
The problem solving process is designed to lead a team from identifying a problem through to delivering a solution and evaluating its effectiveness. Without effective time management skills or timeboxing of tasks, it can be easy for a team to get bogged down or be inefficient.
By using a problem solving model and carefully designing your workshop, you can allocate time efficiently and trust that the process will deliver the results you need in a good timeframe.
Time management also comes into play when it comes to rolling out solutions, particularly those that are experimental in nature. Having a clear timeframe for implementing and evaluating solutions is vital for ensuring their success and being able to pivot if necessary.
Improving your skills at problem solving is often a career-long pursuit though there are methods you can use to make the learning process more efficient and to supercharge your problem solving skillset.
Remember that the skills you need to be a great problem solver have a large overlap with those skills you need to be effective in any role. Investing time and effort to develop your active listening or critical thinking skills is valuable in any context. Here are 7 ways to improve your problem solving skills.
Share best practices
Remember that your team is an excellent source of skills, wisdom, and techniques and that you should all take advantage of one another where possible. Best practices that one team has for solving problems, conducting research or making decisions should be shared across the organization. If you have in-house staff that have done active listening training or are data analysis pros, have them lead a training session.
Your team is one of your best resources. Create space and internal processes for the sharing of skills so that you can all grow together.
Ask for help and attend training
Once you’ve figured out you have a skills gap, the next step is to take action to fill that skills gap. That might be by asking your superior for training or coaching, or liaising with team members with that skill set. You might even attend specialized training for certain skills – active listening or critical thinking, for example, are business-critical skills that are regularly offered as part of a training scheme.
Whatever method you choose, remember that taking action of some description is necessary for growth. Whether that means practicing, getting help, attending training or doing some background reading, taking active steps to improve your skills is the way to go.
Learn a process
Problem solving can be complicated, particularly when attempting to solve large problems for the first time. Using a problem solving process helps give structure to your problem solving efforts and focus on creating outcomes, rather than worrying about the format.
Tools such as the seven-step problem solving process above are effective because not only do they feature steps that will help a team solve problems, they also develop skills along the way. Each step asks for people to engage with the process using different skills and in doing so, helps the team learn and grow together. Group processes of varying complexity and purpose can also be found in the SessionLab library of facilitation techniques . Using a tried and tested process and really help ease the learning curve for both those leading such a process, as well as those undergoing the purpose.
Effective teams make decisions about where they should and shouldn’t expend additional effort. By using a problem solving process, you can focus on the things that matter, rather than stumbling towards a solution haphazardly.
Create a feedback loop
Some skills gaps are more obvious than others. It’s possible that your perception of your active listening skills differs from those of your colleagues.
It’s valuable to create a system where team members can provide feedback in an ordered and friendly manner so they can all learn from one another. Only by identifying areas of improvement can you then work to improve them.
Remember that feedback systems require oversight and consideration so that they don’t turn into a place to complain about colleagues. Design the system intelligently so that you encourage the creation of learning opportunities, rather than encouraging people to list their pet peeves.
While practice might not make perfect, it does make the problem solving process easier. If you are having trouble with critical thinking, don’t shy away from doing it. Get involved where you can and stretch those muscles as regularly as possible.
Problem solving skills come more naturally to some than to others and that’s okay. Take opportunities to get involved and see where you can practice your skills in situations outside of a workshop context. Try collaborating in other circumstances at work or conduct data analysis on your own projects. You can often develop those skills you need for problem solving simply by doing them. Get involved!
Use expert exercises and methods
Learn from the best. Our library of 700+ facilitation techniques is full of activities and methods that help develop the skills you need to be an effective problem solver. Check out our templates to see how to approach problem solving and other organizational challenges in a structured and intelligent manner.
There is no single approach to improving problem solving skills, but by using the techniques employed by others you can learn from their example and develop processes that have seen proven results.
Try new ways of thinking and change your mindset
Using tried and tested exercises that you know well can help deliver results, but you do run the risk of missing out on the learning opportunities offered by new approaches. As with the problem solving process, changing your mindset can remove blockages and be used to develop your problem solving skills.
Most teams have members with mixed skill sets and specialties. Mix people from different teams and share skills and different points of view. Teach your customer support team how to use design thinking methods or help your developers with conflict resolution techniques. Try switching perspectives with facilitation techniques like Flip It! or by using new problem solving methodologies or models. Give design thinking, liberating structures or lego serious play a try if you want to try a new approach. You will find that framing problems in new ways and using existing skills in new contexts can be hugely useful for personal development and improving your skillset. It’s also a lot of fun to try new things. Give it a go!
Encountering business challenges and needing to find appropriate solutions is not unique to your organization. Lots of very smart people have developed methods, theories and approaches to help develop problem solving skills and create effective solutions. Learn from them!
Books like The Art of Thinking Clearly , Think Smarter, or Thinking Fast, Thinking Slow are great places to start, though it’s also worth looking at blogs related to organizations facing similar problems to yours, or browsing for success stories. Seeing how Dropbox massively increased growth and working backward can help you see the skills or approach you might be lacking to solve that same problem. Learning from others by reading their stories or approaches can be time-consuming but ultimately rewarding.
A tired, distracted mind is not in the best position to learn new skills. It can be tempted to burn the candle at both ends and develop problem solving skills outside of work. Absolutely use your time effectively and take opportunities for self-improvement, though remember that rest is hugely important and that without letting your brain rest, you cannot be at your most effective.
Creating distance between yourself and the problem you might be facing can also be useful. By letting an idea sit, you can find that a better one presents itself or you can develop it further. Take regular breaks when working and create a space for downtime. Remember that working smarter is preferable to working harder and that self-care is important for any effective learning or improvement process.
Want to design better group processes?
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Now we’ve explored some of the key problem solving skills and the problem solving steps necessary for an effective process, you’re ready to begin developing more effective solutions and leading problem solving workshops.
Need more inspiration? Check out our post on problem solving activities you can use when guiding a group towards a great solution in your next workshop or meeting. Have questions? Did you have a great problem solving technique you use with your team? Get in touch in the comments below. We’d love to chat!
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The Importance of Problem-Solving Skills in the Workplace
November 10, 2022 - job & career.
Hands-on Management 3.0 leadership workshops focus on tangible practices to help managers, team leaders, middle management, and C-level executives increase employee engagement and foster transformational change within their organizations. Start Your Leadership Journey Today!
According to Management 3.0 Facilitator Ilija Popjanev , problem solving is essential for individuals and organizations as it enables us to control all aspects of our business environment. In this article, Ilija looks into problem-solving skills, how the problem-solving process works, and which tools help you to advance this skill set.
In this article you will learn about:
What is Problem Solving?
- Problem-Solving in Six Easy Steps
Why is Problem-Solving so Important for Leaders, Teams, and Organizations?
Problem-solving techniques in the workplace, better employee experience by using problem-solving tools from management 3.0, how do employees develop problem-solving skills, what skills make a good problem solver.
In the last few years, we have been living 100% in the VUCA world, with so many unpredictable and complex threats and challenges. As a result, organizations must create a sense of urgency to redesign their present business models and to rebuild the foundations for the future of work.
All companies now need effective problem-solving skills and tools at all levels, starting with individuals and teams, and finishing with their leaders and managers. This new reality enables growth and success only for those well-equipped and empowered by effective problem-solving skills and tools.
One of the behaviors of Management 1.0 style is to constantly look for ways to stop “fighting fires,”. Instead, the Management 3.0 style seeks to “find the root cause” of the problem, and then to refocus, improve, and plan a different way for fulfilling workplace tasks.
Management 3.0 provides effective tools and principles for building the system for effective problem solving. It provides us with techniques we can use to understand what is happening in our world, to identify things we want to change, and then apply everything that needs to be done to achieve the desired outcome. We live by the motto: fail fast, recover quickly, and learn from the failures.
The agile way of working does not mean being perfect, but instead it allows for failures and sees them as opportunities to learn, grow, and adapt . Perfection is useless if we do not provide value fast for our customers. That is why problem solving is the foundation for continuous improvement, learning, and collaboration, which leads to innovations and success in ever-changing economies and the new normal that we now live in.
The definition of problem solving according to the Merriam-Webster Dictionary is: “The process or act of finding a solution to a problem.” Similarly, the Oxford English Dictionary describes problem solving as: “The process of finding solutions to difficult or complex issues.”
For me, Problem-solving is a process of understanding and owning the problem, constant pursuit for solutions and improvements, and putting into action the best option for the desired outcome.
Understanding context and interacting with our teammates are the essence of effective problem-solving. We must clearly understand the complexity of our environment and the specifics of the context because things continuously change and evolve. Here, the Complexity Thinking Guidelines may help you to better understand what is happening and how to navigate complex environments more effectively.
We must have a lens through which to see problems as opportunities to improve, and regard our teams as sources of knowledge and experience. We have to connect people and opportunities in ways that can facilitate the best solutions for the problems that we are handling. Try using the Personal Maps , an excellent tool for bringing teams together and fostering diversity, respect, trust, and collaboration.
Today, all innovations and solved problems are team efforts because teams constantly improve their toolbox and competencies. Teams want to create something that was not there before, and which maximize their knowledge and resources.
To accomplish that, they need to build a process in a few easy steps:
- Be present, observe what is happening in your world, and define the problem.
- Review where you are now and what influences that state.
- Constantly improve and change things by using creative tools and tactics.
- Seek solutions and alternatives to make changes more effective.
- Make team decisions about which tools and solutions should be used.
- Implement improvements, monitor the process, and constantly adapt!
At this stage, by following the Management 3.0 principle of “Improving the system,” you can use the tools Celebration Grids , combined with Yay! Questions , to best engage the team in the problem-solving process, while keeping track of what is working well, what can be changed, and what new options exist.
Documenting everything is an integral part of the problem-solving process. By using Celebration Grids, you are gamifying the process and keeping the team flow and energy on a higher level.
Also read: What type of problem-solver are you?
Problem-solving is crucial for everyone: individuals, teams, leaders, organizations, and ultimately for all stakeholders because it empowers us to better control the environment and everything that is going on in our world. Try using Delegation Poker so that teams can become more empowered to solve problems both alongside leaders and within their organization.
Today, the speed of problem solving is important, and that is why organizations must give more power and authority on a team level , so employees can react quickly and even prevent problems. As a leading indicator, the Management 3.0 tool Problem Time can help you measure the time spent on uncompleted problem-solving tasks and activities; this is a valuable add-on to “lead and cycle time” lagging indicators, with which you measure the time taken on completed tasks.
Developing and refining problem-solving skills through constant practice and experimentation can refine the ability to solve problems and address issues with more complexities.
We may face various challenges in our daily work, and effective problem-solving can make a difference.
Make a Difference with Problem-Solving
- Problem-solving skills are important if you want to add more value . As an agilist, your objective is not to be perfect but to maximize the value you provide for all stakeholders. Start fast, deliver value early, manage failures and prioritize tasks by setting the urgency criteria.
- Problem-solving skills are important if you need to improve your results. You have to accept the complexity of success factors and better understand the need for changes and improvements in a continually uncertain environment. Results depend on your problem-solving skills!
- Problem-solving skills are important if you have to fix things that do not work. When your processes are not working as planned, problem solving will give you the structure and mechanisms to identify issues, figure out why things are broken, and take actions to fix them.
- Problem-solving skills are important when you have to address a risk. Sharpen your problem-solving skills to anticipate future events better and increase the awareness of cause-and-effect relationships. This enables you to take the right actions and influence the outcomes if issues do occur.
- Problem-solving skills are important if you work simultaneously on several projects. You should apply the same problem-solving techniques when you work on multiple projects, business functions, market segments, services, systems, processes, and teams. Standardize and scale!
- Problem-solving skills are important when you want to seize the day. Problem solving is all about innovation , building new things, and changing the system into a better one. This can help us to identify opportunities even in challenging times and prepare us for the future. You can visualize the process with the Meddles Game to better understand your ideas, solutions, and activities. It is a great way to engage your team as you can build the problem-solving concept and it is an effective tool for influencing all stakeholders affected by the problem.
Also read: Collaborative Leadership explained .
Solving complex problems may be difficult, but problems will be solved when we use the right tools. Besides the powerful Management 3.0 tools I already mentioned, as a big fan of Lean and Liberating structures, I think you can find lots of problem-solving techniques to use in your daily business.
Here is my short list of tools and techniques:
- 5 Whys – a great way to uncover the root cause is to understand the problem better.
- Fishbone analysis – for visual analysis of the root causes of a problem. Easy to combine with ‘5 Whys’ or ‘Mind mapping’ to brainstorm and determine the cause and effect of any problem.
- Silent brainstorming – gives everyone a chance to participate in idea generation as not only the loudest people, but also the quiet ones, will participate equally. Everyone’s opinion has the same weight.
- Mind maps – structured visual diagrams to share your ideas, concepts, and solutions the same way your brain does. You explain the problems quickly, then share fresh ideas, and finally come to a team consensus that can lead to an effective solution.
- Six thinking hats – enable your team to consider problems from different angles, focusing on facts, creative solutions, or why some solutions might not work.
- Agreement certainty matrix – another tremendous visual tool for brainstorming problems and challenges by sorting them into simple, complicated, complex, or chaotic domains to later agree on what approach should be used to solve the concrete problems affecting a team.
- Conversation café – enables the team to engage in productive conversations, with less arguing but more active listening, solving the problem in rounds of dialogues until reaching a consensus regarding the best problem-solving approach.
- Design thinking – when you are struggling for fresh ideas, the 5-step process will help you empathize with the problem, then begin defining and developing new ideas, before prototyping and testing them.
Edward Deming’s PDCA is the most known concept for continuous improvement and problem solving. You can gamify your events using the Change Management Game , a card game where PDCA will help you define the problem, take action, collect feedback, and adopt the new solution.
The “carrot and stick” approach, or in HR language, “pay for performance,” does not work anymore, especially for roles that require problem-solving, creativity, and innovative thinking. Creative people need a higher level of authority and empowerment to self-manage challenges and problem scenarios. When leaders and organizations create such systems, they foster intrinsic motivation and job satisfaction among these people. Creatives are seeking self-actualization through their careers.
This is one more case which calls for Management 3.0’s Delegation Poker to define the levels of authority in terms of problem-solving issues, as well as Moving Motivators to define key motivators for increasing productivity and employee satisfaction by changing behavior.
Improving Employee Experience with Problem-Solving
1. Use problem solving as a key motivator – have in mind Millennials and Gen Z creative workers ’ affinity towards tasks in which they feel challenged and have a sense of meaning. Provide them with big and tough problems to solve and use challenging tasks to keep them constantly engaged.
2. Continuous improvement can make a difference – creatives seek a sense of purpose and think outside of the box, so encouraging the ‘How can we execute this task better?’ mindset and problem solving become powerful tools for creating sustainable corporate culture.
3. Don’t connect solving problems with rewards – it can kill the perceived intrinsic value of the activity; it will disengage and dissatisfy employees. Autonomy, trust, respect, and gratitude will do the job.
4. Apply the seven rules for creative managers – unleash the power of diversity , and cooperation, rely on merits, optimize exploration, open boundaries, keep options open, and update your workplace.
We start solving problems from a very early age (the alphabet, learning to eat, driving a bicycle etc.). Then, everyday activities sharpen our problem-solving skills and enable us to solve more complex issues.
As an adult, you can still develop your problem-solving skills by:
- Daily practicing of logic games, such as chess, and puzzles like Sudoku.
- Video games can teach you how to deal with failure and persist in achieving your goals.
- Keep an idea journal or blog as a collection of all your ideas, thoughts, and patterns.
- Think outside of the box – take a different perspective to understand the problem better.
- Practice brainstorming combined with mind mapping, working with your team.
- Put yourself in new situations – take on a challenging project at work.
- Start using the “what if” mindset in daily circumstances and test new approaches.
- Read more books on creativity and articles which cover your areas of interest.
I also believe coaching can help build creativity and problem-solving skills, encouraging people to take greater ownership of their work and commit to corporate goals. A coach can provide clear guidance as to what is important at the moment; they help people better, focus, and move into action. By asking powerful questions and challenging others to think outside of the box, the coach removes their barriers and lets them see the situation from a new perspective.
Coaching can provide structure so people develop their own expertise and insights to contribute better when problems arise and the pressure to succeed is growing.
The interview is an excellent opportunity to research a candidate’s problem-solving skills, and STAR questions should be related to their previous experience dealing with problems. A candidate with good problem-solving skills can quickly embed in the team and become a valuable asset for the company.
In my Agility in HR workshops , we regularly discuss interview questions. Some popular STAR questions are:
- “If you cannot find a solution to a problem, how do you deal with the situation?”
- “How do you react when faced with unexpected problems or challenges?”
- “Describe an occasion when you had to adapt at the last minute. How did you handle this?”
Problem-solving requires the ability to identify a problem, find the root cause, create solutions, and execute them. All these steps are essential for achieving the desired results.
Some of the skills that problem solvers must constantly sharpen are:
- Collaborative communication . Clear communication is essential when you explain the problem and the solution to your teammates. During brainstorming sessions, asking the right questions to determine the root cause , as well as synergic collaboration are needed.
- Active listening is important to prevent mistakes as you can absorb the details your colleagues tell you about the problem. Use open-ended questions for clarification, and always be open to feedback and views that differ from yours.
- Coachability. The willingness to accept feedback and the ability to improve. Learning from more experienced people, being curious to ask many questions, constructively using your ego, skipping excuses and blaming others, and accepting Feedback Wraps from your coach.
- Decision making . Problems cannot be solved without risk-taking and bringing important decisions (including relevant data, levels of delegation, alternative solutions etc.) to the forefront.
- Critical thinking . Be 100% objective when you try to find the cause of the problem. Skip ego trips and personal biases. Identify your mistakes in the thinking process and show personal accountability .
- Research and data analysis . Proper research allows you to diagnose the actual problem, not just the symptoms. If the cause of the problem is not immediately apparent, you can use the power of data to discover the issue’s history, some patterns, future trends, etc.
- Persistence . Trust in the problem-solving process you have designed and follow every step with patience and persistence; even when you fail repeatedly, do not give up. Keep moving and remember Thomas Edison’s quote: “I have not failed. I have just found 9,999 ways that do not work.”
In the new VUCA world we now live in, problem solving is a crucial soft skill, and employers are actively seeking people with this skill set because they can prepare for problems before they arise. Problem solvers better identify opportunities, understand their environment, create a solution, and generate ideas that lead to great results and success.
According to a study made by LinkedIn Learning in August 2022 , future skills are rapidly changing, and problem solving is among the top soft skills employers search for from their candidates, as well as communication and leadership skills.
Using all aforementioned tools and practices from Management 3.0, following the guides, and sharpening your skills, will help you not only to be effective in resolving the problems that may arise, but also to solve them with enthusiasm and passion. They will create a higher level of engagement and collaboration in the team and help unleash people’s creativity and innovation. A win-win for everyone!
Photo by Parabol on Unsplash
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The Efficacy and Development of Students' Problem-Solving Strategies During Compulsory Schooling: Logfile Analyses
1 Department of Learning and Instruction, University of Szeged, Szeged, Hungary
2 MTA-SZTE Research Group on the Development of Competencies, University of Szeged, Szeged, Hungary
The purpose of this study was to examine the role of exploration strategies students used in the first phase of problem solving. The sample for the study was drawn from 3 rd - to 12 th -grade students (aged 9–18) in Hungarian schools ( n = 4,371). Problems designed in the MicroDYN approach with different levels of complexity were administered to the students via the eDia online platform. Logfile analyses were performed to ascertain the impact of strategy use on the efficacy of problem solving. Students' exploration behavior was coded and clustered through Latent Class Analyses. Several theoretically effective strategies were identified, including the vary-one-thing-at-a-time (VOTAT) strategy and its sub-strategies. The results of the analyses indicate that the use of a theoretically effective strategy, which extract all information required to solve the problem, did not always lead to high performance. Conscious VOTAT strategy users proved to be the best problem solvers followed by non-conscious VOTAT strategy users and non-VOTAT strategy users. In the primary school sub-sample, six qualitatively different strategy class profiles were distinguished. The results shed new light on and provide a new interpretation of previous analyses of the processes involved in complex problem solving. They also highlight the importance of explicit enhancement of problem-solving skills and problem-solving strategies as a tool for knowledge acquisition in new contexts during and beyond school lessons.
Computer-based assessment has presented new challenges and opportunities in educational research. A large number of studies have highlighted the importance and advantages of technology-based assessment over traditional paper-based testing (Csapó et al., 2012 ). Three main factors support and motivate the use of technology in educational assessment: (1) the improved efficiency and greater measurement precision in the already established assessment domains (e.g., Csapó et al., 2014 ); (2) the possibility of measuring constructs that would be impossible to measure by other means (e.g., Complex Problem Solving (CPS) 1 ; see Greiff et al., 2012 , 2013 ); and (3) the opportunity of logging and analyzing not only observed variables, but metadata as well (Lotz et al., 2017 ; Tóth et al., 2017 ; Zoanetti and Griffin, 2017 ). Analyzing logfiles may contribute to a deeper and better understanding of the phenomenon under examination. Logfile analyses can provide answers to research questions which could not be answered with traditional assessment techniques.
This study focuses on problem solving, especially on complex problem solving (CPS), which reflects higher-order cognitive processes. Previous research identified three different ways to measure CPS competencies: (1) Microworlds (e.g., Gardner and Berry, 1995 ), (2) formal frameworks (Funke, 2001 , 2010 ) and (3) minimal complex systems (Funke, 2014 ). In this paper, the focus is on the MicroDYN approach, which is a specific form of complex problem solving (CPS) in interactive situations using minimal complex systems (Funke, 2014 ). Recent analyses provide both a new theory and data-based evidence for a global understanding of different problem-solving strategies students employ or could employ in a complex problem-solving environment based on minimal complex systems.
The problem scenarios within the MicroDYN approach consist of a small number of variables and causal relations. From the perspective of the problem solver, solving a MicroDYN problem requires a sequence of continuous activities, in which the outcome of one activity is the input for the next. First, students interact with the simulated system, set values for the input variables, and observe the impacts of these settings on the target (dependent) variable. Then, they plot their conclusion about the causal relationships between the input and output variables on a graph (Phase 1). Next, they manipulate the independent variables again to set their values so that they result in the required values for the target variables (Phase 2).
When it comes to gathering information about a complex problem, as in the MicroDYN scenarios, there may be differences between the exploration strategies in terms of efficacy. Some of them may be more useful for generating knowledge about the system. Tschirgi ( 1980 ) identified different exploration strategies. When control of variables strategies (Greiff et al., 2014 ) were explored, findings showed that the vary-one-thing-at-a-time (VOTAT, Tschirgi, 1980 ; Funke, 2014 ) was the most effective strategy for identifying causal relations between the input and output variables in a minimal complex system (Fischer et al., 2012 ). Participants who employed this strategy tended to acquire more structural knowledge than those who used other strategies (Vollmeyer et al., 1996 ; Kröner et al., 2005 ). With the VOTAT strategy, the problem solver systematically varies only one input variable, while the others remain unchanged. This way, the effect of the variable that has just been changed can be observed directly by monitoring the changes in the output variables. There exist several types of VOTAT strategies.
Using this approach—defining the effectiveness of a strategy on a conceptual level, independently of empirical effectiveness—we developed a labeling system and a mathematical model based on all theoretically effective strategies. Thus, effectiveness was defined and linked to the amount of information extracted. An exploration strategy was defined as theoretically effective if the problem solver was able to extract all the information needed to solve the problem, independently of the application level of the information extracted and of the final achievement. We split the effectiveness of the exploration strategy and the usage and application of the information extracted to be able to solve the problem and control the system with respect to the target values based on the causal knowledge acquired. Systematicity was defined on the level of effectiveness based on the amount of information extracted and on the level of awareness based on the implementation of systematicity in time.
Students' actions were logged and coded according to our input behavior model and then clustered for comparison. We were able to distinguish three different VOTAT strategies and two successful non-VOTAT ones. We empirically tested awareness of the input behavior used in time. Awareness of strategy usage was analyzed by the sequence of the trials used, that is, by the systematicity of the trials used in time. We investigated the effectiveness of and differences in problem-solving behavior between three age groups by conducting latent class analyses to explore and define patterns in qualitatively different VOTAT strategy uses.
Although the assessment of problem solving within the MicroDYN approach is a relatively new area of research, its processes have already been studied in a number of different contexts, including a variety of educational settings with several age groups. Our cross-sectional design allows us to describe differences between age groups and outline the developmental tendencies of input behavior and strategy use among children in the age range covered by our data collection.
Reasoning strategies in complex problem solving
Problem-solving skills have been among the most extensively studied transversal skills over the last decade; they have been investigated in the most prominent comprehensive international large-scale assessments today (e.g., OECD, 2014 ). The common aspects in the different theoretical models are that a problem is characterized by a gap between the current state and the goal state with no immediate solution available (Mayer and Wittrock, 1996 ).
Parallel to the definition of the so-called twenty first-century skills (Griffin et al., 2012 ), recent research on problem solving disregards content knowledge and domain-specific processes. The reason for this is that understanding the structure of unfamiliar problems is more effective when it relies on abstract representation schemas and metacognitive strategies than on specifically relevant example problems (Klahr et al., 2007 ). That is, the focus is more on assessing domain-general problem-solving strategies (Molnár et al., 2017 ), such as complex problem solving, which can be used to solve novel problems, even those arising in interactive situations (Molnár et al., 2013 ).
Logfile analyses make it possible to divide the continuum of a problem-solving process into several scoreable phases by extracting information from the logfile that documents students' problem-solving behavior. In our case, latent class analysis extracts information from the file that logs students' interaction with the simulated system at the beginning of the problem-solving process. The way students manipulate the input (independent) variables represents their reasoning strategy. Log data, on the one hand, make it possible to analyze qualitative differences in these strategies and then their efficiency in terms of how they generate knowledge resulting in the correct plotting of the causal relationship in Phase 1 and then the proper setting to reach the required target value in Phase 2. On the other hand, qualitative strategy data can be quantified, and an alternative scoring system can be devised.
From the perspective of the traditional psychometric approach and method of scoring, these problems form a test task consisting of two scoreable items. The first phase is a knowledge acquisition process, where scores are assigned based on how accurately the causal relationship was plotted. The second phase is knowledge application, where the correctness of the value for the target variable is scored. Such scoring based on two phases of solving MicroDYN problems has been used in a number of previous studies (e.g., Greiff et al., 2013 , 2015 ; Wüstenberg et al., 2014 ; Csapó and Molnár, 2017 ; Greiff and Funke, 2017 ).
To sum up, there is great potential to investigate and cluster the problem-solving behavior and exploration strategy usage of the participants at the beginning of the problem-solving process and correlate the use of a successful exploration strategy with the model-building solution (achievement in Phase 1) observed directly in these simulated problem scenarios. Using logfile analyses (Greiff et al., 2015 ), the current article wishes to contribute insights into students' approaches to explore and solve problems related to minimal complex systems. By addressing research questions on the problem-solving strategies used, the study aims to understand students' exploration behavior in a complex problem-solving environment and the underlying causal relations. In this study, we show that such scoring can be developed through latent class analysis and that this alternative method of scoring may produce more reliable tests. Furthermore, such scoring can be automated and then employed in a large-scale assessment.
There are two major theoretical approaches to cognition relevant to our study; both offer general principles to interpret cognitive development beyond the narrower domain of problem solving. Piaget proposed the first comprehensive theory to explain the development of children's thinking as a sequence of four qualitatively different stages, the formal operational stage being the last one (Inhelder and Piaget, 1958 ), while the information processing approach describes human cognition by using terms and analogies borrowed from computer science. The information processing paradigm was not developed into an original developmental theory; it was rather aimed at reinterpreting and extending Piaget's theory (creating several Neo-Piagetian models) and synthesizing the main ideas of the two theoretical frameworks (Demetriou et al., 1993 ; Siegler, 1999 ). One of the focal points of these models is to explain the development of children's scientific reasoning, or, more closely, the way children understand how scientific experiments can be designed and how causal relationships can be explored by systematically changing the values of (independent) variables and observing their impact on other (target) variables.
From the perspective of the present study, the essential common element of cognitive developmental research is the control of variables strategy. Klahr and Dunbar ( 1988 ) distinguished two related skills in scientific thinking, hypothesis formation and experimental design, and they integrated these skills into a coherent model for a process of scientific discovery. The underlying assumption is that knowledge acquisition requires an iterative process involving both. System control as knowledge application tends to include both processes, especially when acquired knowledge turns out to be insufficient or dysfunctional (J. F. Beckmann, personal communication, August 16, 2017). Furthermore, they separated the processes of rule induction and problem solving, defining the latter as a search in a space of rules (Klahr and Dunbar, 1988 , p. 5).
de Jong and van Joolingen ( 1998 ) provided an overview of studies in scientific discovery learning with computer simulations. They concluded that a number of specific skills are needed for successful discovery, like systematic variation of variable values, which is in a focus of the present paper, and the use of high-quality heuristics for experimentation. They identified several characteristic problems in the discovery process and stressed that learners often have trouble interpreting data.
In one of the earliest systematic studies of students' problem-solving strategies, Vollmeyer et al. ( 1996 ) explored the impact of strategy systematicity and effectiveness on complex problem-solving performance. Based on previous studies, they distinguished the VOTAT strategy from other possible strategies [Change All (CA) and Heterogeneous (HT) other strategies], as VOTAT allows systematic exploration of the behavior of a system and a disconfirmation of hypotheses. In one of their experiments, they examined the hypothesis that VOTAT was more effective for acquiring knowledge than less systematic strategies. According to the results, the 36 undergraduate students had clearly shown strategy development. After interacting with the simulated system in several rounds, they tended to use the VOTAT strategy more frequently. In a second experiment, it was also demonstrated that goal specificity influences strategy use as well (Vollmeyer et al., 1996 ).
Beckmann and Goode ( 2014 ) analyzed the systematicity in exploration behavior in a study involving 80 first-year psychology students and focusing on the semantic context of a problem and its effect on the problem solvers' behavior in complex and dynamic systems. According to the results, a semantically familiar problem context invited a high number of a priori assumptions on the interdependency of system variables. These assumptions were less likely tested during the knowledge acquisition phase, this proving to be the main barrier to the acquisition of new knowledge. Unsystematic exploration behavior tended to produce non-informative system states that complicated the extraction of knowledge. A lack of knowledge ultimately led to poor control competency.
Beckmann et al. ( 2017 ) confirmed research results by Beckmann and Goode ( 2014 ) and demonstrated how a differentiation between complexity and difficulty leads to a better understanding of the cognitive mechanism behind CPS. According to findings from a study with 240 university students, the performance differences observed in the context of the semantic effect were associated with differences in the systematicity of the exploration behavior, and the systematicity of the exploration behavior was reflected in a specific sequence of interventions. They argued that it is only the VOTAT strategy—supplemented with the vary- none -at-a-time strategy in the case of noting autonomous changes—that creates informative system state transitions which enable problem solvers to derive knowledge of the causal structure of a complex, dynamic system.
Schoppek and Fischer ( 2017 ) also investigated VOTAT and the related “PULSE” strategy (all input variables to zero), which enables the problem solver to observe the eigendynamics of the system in a transfer experiment. They proposed that besides VOTAT and PULSE, other comprehensive knowledge elements and strategies, which contribute to successful CPS, should be investigated.
In a study with 2 nd - to 4 th -grade students, Chen and Klahr found little spontaneous development when children interacted with physical objects (in situations similar to that of Piaget's experiments), while more direct teaching of the control of variables strategy resulted in good effect sizes and older children were able to transfer the knowledge they had acquired (improved control of variable strategy) to remote contexts (Chen and Klahr, 1999 ). In a more recent study, Kuhn et al. ( 2008 ) further extended the scope of studies on scientific thinking, identifying three further aspects beyond the control of variables strategy, including coordinating effects of multiple influences, understanding the epistemological foundations of science and engaging in argumentation. In their experiment with 91 6th-grade students, they explored how students were able to estimate the impact of five independent variables simultaneously on a particular phenomenon, and they found that most students considered only one or two variables as possible causes.
In this paper, we explore several research questions on effective and less effective problem-solving strategies used in a complex problem-solving environment and detected by logfile analyses. We use logfile analyses to empirically test the success of different input behavior and strategy usage in CPS tasks within the MicroDYN framework. After constructing a mathematical model based on all theoretically effective strategies, which provide the problem solver with all the information needed to solve the problem, and defining several sub-strategies within the VOTAT strategy based on the amount of effort expended to extract the necessary information, we empirically distinguish different VOTAT and non-VOTAT strategies, which can result in good CPS performance and which go beyond the isolated variation strategy as an effective strategy for rule induction (Vollmeyer et al., 1996 ). We highlight the most and least effective VOTAT strategies used in solving MicroDYN problems and empirically investigate the awareness of the strategy used based on the sequence of the sub-strategies used. Based on these results, we conduct latent class analyses to explore and define patterns in qualitatively different VOTAT strategy uses.
We thus intend to answer five research questions:
- RQ1: Does the use of a theoretically effective strategy occur prior to high performance? In other words, does the use of a theoretically effective strategy result in high performance?
- RQ2: Do all VOTAT strategies result in a high CPS performance? What is the most effective VOTAT strategy?
- RQ3: How does awareness of the exploration strategy used influence overall performance on CPS tasks?
- RQ4: What profiles characterize the various problem solvers and explorers?
- RQ5: Do exploration strategy profiles differ across grade levels, which represent different educational stages during compulsory schooling?
In this study, we investigated qualitatively different classes of students' exploration behavior in CPS environments. We used latent class analysis (LCA) to study effective and non-effective input behavior and strategy use, especially the principle of isolated variation, across several CPS tasks. We compared the effectiveness of students' exploration behavior based on the amount of information they extracted with their problem-solving achievement. We posed five separate hypotheses.
Hypothesis 1: We expect that high problem-solving achievement is not closely related to expert exploration behavior.
Vollmeyer et al. ( 1996 ) explored the impact of strategy effectiveness on problem-solving performance and reported that effectiveness correlated negatively and weakly to moderately with solution error ( r = −0.32 and r = −0.54, p < 0.05). They reported that “most participants eventually adopted the most systematic strategy, VOTAT, and the more they used it, the better they tended to perform. However, even those using the VOTAT strategy generally did not solve the problem completely” (p. 88). Greiff et al. ( 2015 ) confirmed that different exploration behaviors are relevant to CPS and that the number of sub-strategies implemented was related to overall problem-solving achievement.
Hypothesis 2: We expect that students who use the isolated variation strategy in exploring CPS problems have a significantly better overall performance than those who use a theoretically effective, but different strategy.
Sonnleiter et al. ( 2017 ) noted that “A more effective exploration strategy leads to a higher system knowledge score and the higher the gathered knowledge, the better the ability to achieve the target values. Thus, system knowledge can be seen as a reliable and valid measure of students' mental problem representations” (p. 169). According to Wüstenberg et al. ( 2012 ), students who consistently apply the principle of isolated variation—the most systematic VOTAT strategy—in CPS environments show better overall CPS performance, compared to those who use different exploration strategies. Kröner et al. ( 2005 ) reported a positive correlation between using the principle of isolated variation and the likelihood of solving the overall problem.
Hypothesis 3: We expected that more aware CPS exploration behavior would be more effective than exploration behavior that generally results in extracting all the necessary information from the system to solve the problem, but within which the steps have no logically built structure and no systematicity in time.
Vollmeyer et al. ( 1996 ) explored the impact of strategy systematicity on problem-solving performance. They emphasized that “the systematicity of participants' spontaneous hypothesis-testing strategies predicted their success on learning the structure of the biology lab problem space” (p. 88). Vollmeyer and her colleagues restricted systematic strategy users to isolated variation strategy users; this corresponds to our terminology usage of aware isolated variation strategy users.
Hypothesis 4: We expected to find a distinct number of classes with statistically distinguishable profiles of CPS exploration behavior. Specifically, we expected to find classes of proficient, intermediate and low-performing explorers.
Several studies (Osman and Speekenbrink, 2011 ; Wüstenberg et al., 2012 ; Greiff et al., 2015 ) have indicated that there exist quantitative differences between different exploration strategies, which are relevant to a CPS environment. The current study is the first to investigate whether a relatively small number of qualitatively different profiles of students' exploration proficiency can be derived from their behavior detected in a CPS environment in a broad age range.
Hypothesis 5: We expected that more proficient CPS exploration behavior would be more dominant at later grade levels as an indication of cognitive maturation and of increasing abilities to explore CPS environments.
The cognitive development in children between Grades 3 and 12 is immense. According to Piaget's stage theory, they move from concrete operations to formal operations and they will be able to think logically and abstractly. According to Galotti ( 2011 ) and Molnár et al. ( 2013 ), the ability to solve problems effectively and to make decisions in CPS environments increases in this period of time; Grades 6–8 seem especially crucial for development. Thus, we expect that cognitive maturation will also be reflected in more proficient exploration behavior.
The sample was drawn from 3 rd - to 12 th -grade students (aged 9–18) in Hungarian primary and secondary schools ( N = 4,371; Table Table1). 1 ). School classes formed the sampling unit. 180 classes from 50 schools in different regions were involved in the study, resulting in a wide-ranging distribution of students' background variables. The proportion of boys and girls was about the same.
Composition of samples.
The MicroDYN approach was employed to develop a measurement device for CPS. CPS tasks within the MicroDYN approach are based on linear structural equations (Funke, 2001 ), in which up to three input variables and up to three output variables are related (Greiff et al., 2013 ). Because of the small set of input and output variables, the MicroDYN problems could be understood completely with precise causal analyses (Funke, 2014 ). The relations are not presented to the problem solver in the scenario. To explore these relations, the problem solver must interact directly with the problem situation by manipulating the input variables (Greiff and Funke, 2010 ), an action that can influence the output variables (direct effects), and they must use the feedback provided by the computer to acquire and employ new knowledge (Fischer et al., 2012 ). Output variables can change spontaneously and can consist of internal dynamics, meaning they can change without changing the input variables (indirect effects; Greiff et al., 2013 ). Both direct and indirect effects can be detected with an adequate problem-solving strategy (Greiff et al., 2012 ). The interactions between the problem situation and the test taker play an important role, but they can only be identified in a computerized environment based on log data collected during test administration.
In this study, different versions with different levels of item complexity were used (Greiff et al., 2013 ), which varied by school grade (Table (Table2; 2 ; six MicroDYN scenarios were administered in total in Grades 3–4; eight in Grade 5: nine in Grades 6–8; and twelve in Grades 9–12); however, we only involved those six tasks where the principle of isolated variation was the optimal exploration strategy. That is, we excluded problems with an external manipulation-independent, internal dynamic effect or multiple dependence effect from the analyses, and there were no delayed or accumulating effects used in the problem environments created. Complexity was defined by the number of input and output variables and the number of relations based on Cognitive Load Theory (Sweller, 1994 ). “Findings show that increases in the number of relations that must be processed in parallel in reasoning tasks consistently lead to increases in task difficulty” (Beckmann and Goode, 2017 ).
The design of the whole study: the complexity of the systems administered and the structure and anchoring of the tests applied in different grades.
The tasks were designed so that all causal relations could be identified with systematic manipulation of the inputs. The tasks contained up to three input variables and up to three output variables with different fictitious cover stories. The values of the input variables were changed by clicking on a button with a + or – sign or by using a slider connected to the respective variable (see Figure Figure1). 1 ). The controllers of the input variables range from “– –” (value = −2) to “++” (value = +2). The history of the values of the input variables within the same scenario was presented on a graph connected to each input variable. Beyond the input and output variables, each scenario contained a Help, Reset, Apply and Next button. The Reset button set the system back to its original status. The Apply button made it possible to test the effect of the currently set values of the input variables on the output variables, which appeared in the form of a diagram of each output variable. According to the user interface, within the same phase of each of the problem scenarios, the input values remained at the level at which they were set for the previous input until the Reset button was pressed or they were changed manually. The Next button implemented the navigation between the different MicroDYN scenarios and the different phases within a MicroDYN scenario.
Exploration in phase 1 of the MicroDYN problems (two input variables and two output variables).
In the knowledge acquisition phase, participants were freely able to change the values of the input variables and attempt as many trials for each MicroDYN scenario as they liked within 180 s. During this 180 s, they had to draw the concept map (or causal diagram; Beckmann et al., 2017 ); that is, they had to draw the arrows between the variables presented on the concept map under the MicroDYN scenario on screen. In the knowledge application phase, students had to check their respective system using the right concept map presented on screen by reaching the given target values within a given time frame (90 s) in no more than four trials, that is, with a maximum of four clicks on the Apply button. This applied equally to all participants.
All of the CPS problems were administered online via the eDia platform. At the beginning, participants were provided with instructions about the usage of the user interface, including a warm-up task. Subsequently, participants had to explore, describe and operate unfamiliar systems. The assessment took place in the schools' ICT labs using the available school infrastructure. The whole CPS test took approximately 45 min to complete. Testing sessions were supervised by teachers who had been thoroughly trained in test administration. Students' problem-solving performance in the knowledge acquisition and application phases was automatically scored as CPS performance indicators; thus, problem solvers received immediate performance feedback at the end of the testing session. We split the sample into three age groups, whose achievement differed significantly (Grades 3–5, N = 1,871; Grades 6–7, N = 1,284; Grades 8–12, N = 1,216; F = 122.56, p < 0.001; t level_1_2 = −6.22, p < 0.001; t level_2_3 = −8.92, p < 0.001). This grouping corresponds to the changes in the developmental curve relevant to complex problem solving. The most intensive development takes place in Grades 6–7 (see Molnár et al., 2013 ). Measurement invariance, that is, the issue of structural stability, has been demonstrated with regard to complex problem solving in the MicroDYN approach already (e.g., Greiff et al., 2013 ) and was confirmed in the present study (Table (Table3). 3 ). Between group differences can be interpreted as true and not as psychometric differences in latent ability. The comparisons across grade levels are valid.
Goodness of fit indices for measurement invariance of MicroDYN problems.
χ 2 and df were estimated by the weighted least squares mean and variance adjusted estimator (WLSMV). Δχ 2 and Δdf were estimated by the Difference Test procedure in MPlus. Chi-square differences between models cannot be compared by subtracting χ 2 s and dfs if WLSMV estimators are used. CFI, comparative fit index; TLI, Tucker Lewis index; RMSEA, root mean square error of approximation .
The latent class analysis (Collins and Lanza, 2010 ) employed in this study seeks students whose problem-solving strategies show similar patterns. It is a probabilistic or model-based technique, which is a variant of the traditional cluster analysis (Tein et al., 2013 ). The indicator variables observed were re-coded strategy scores. Robust maximum likelihood estimation was used and two to seven cluster solutions were examined. The process of latent class analysis is similar to that of cluster analysis. Information theory methods, likelihood ratio statistical test methods and entropy-based criteria were used in reducing the number of latent classes. As a measure of the relative model fit, AIC (Akaike Information Criterion), which considers the number of model parameters, and BIC (Bayesian Information Criterion), which considers the number of parameters and the number of observations, are the two original and most commonly used information theory methods for model selection. The adjusted Bayesian Information Criterion (aBIC) is the sample size-adjusted BIC. Lower values indicated a better model fit for each criterion (see Dziak et al., 2012 ). Entropy represents the precision of the classification for individual cases. MPlus reports the relative entropy index of the model, which is a re-scaled version of entropy on a [0,1] scale. Values near one, indicating high certainty in classification, and values near zero, indicating low certainty, both point to a low level of homogeneity of the clusters. Finally, the Lo–Mendell–Rubin Adjusted Likelihood Ratio Test (Lo et al., 2001 ) was employed to compare the model containing n latent classes with that containing n −1 latent classes. A significant p -value ( p < 0.05) indicates that the n −1 model is rejected in favor of a model with n classes, as it fits better than the previous one (Muthén and Muthén, 2012 ).
As previous research has found (Greiff et al., 2013 ), achievement in the first and second phases of the problem-solving process can be directly linked to the concept of knowledge acquisition (representation) and knowledge application (generating a solution) and was scored dichotomously. For knowledge acquisition, students' responses were scored as correct (“1”) if the connections between the variables were accurately indicated on the concept map (students' drawings fully matched the underlying problem structure); otherwise, the response was scored as incorrect (“0”). For knowledge application, students' responses were scored as correct (“1”) if students reached the given target values within a given time frame and in no more than four steps, that is, with a maximum of four clicks on the Apply button; otherwise, the response was scored as incorrect (“0”).
We developed a labeling procedure to divide the continuum of the problem-solving process into more scoreable phases and to score students' activity and behavior in the exploration phase at the beginning of the problem-solving process. For the different analyses and the most effective clustering, we applied a categorization, distinguishing students' use of the full, basic and minimal input behavior within a single CPS task (detailed description see later). The unit of this labeling process was a trial, a setting of the input variables, which was tested by clicking on the Apply button during the exploration phase of a problem, thus between receiving the problem and clicking on the Next button to reach the second part, the application part of the problem. The sum of these trials, within the same problem environment is called the input behavior. The input behavior was called a strategy if it followed meaningful regularities.
By our definition, the full input behavior model describes what exactly was done throughout the exploration phase and what kinds of trials were employed in the problem-solving process. It consists of all the activities with the sliders and Apply buttons in the order they were executed during the first phase, the exploration phase of the problem-solving process. The basic input behavior is part of the full input behavior model by definition, when the order of the trials attempted was still being taken into account, but it only consists of activities where students were able to acquire new information on the system. This means that the following activities and trials were not included in the basic input behavior model (they were deleted from the full input behavior model to obtain the basic behavior model):
- - where the same scenario, the same slider adjustment, was employed earlier within the task (that is, we excluded the role of ad hoc control behavior from the analyses),
- - where the value (position) of more than one input variable (slider) was changed and where the effect of the input variable on the operation of the system was still theoretically unknown to the problem solver,
- - where a new setting or new slider adjustment was employed, though the effect of the input variables used was known from previous settings.
- - As the basic input behavior involves timing, that is, the order of the trials used, it is suitable for the analyses with regard to the awareness of the input behavior employed.
Finally, we generated the students' minimal input behavior model from the full input behavior model. By our definition, the minimal input behavior focuses on those untimed activities (a simple list, without the real order of the trials), where students were able to obtain new information from the system and were able to do so by employing the most effective trials.
Each of the activities in which the students engaged and each of the trials which they used were labeled according to the following labeling system to be able to define students' full input behavior in a systematic format (please note that the numerical labels are neither scores nor ordinal or metric information):
- Only one single input variable was manipulated, whose relationship to the output variables was unknown (we considered a relationship unknown if its effect cannot be known from previous settings), while the other variables were set at a neutral value like zero. We labeled this trial +1.
- One single input variable was changed, whose relationship to the output variables was unknown. The others were not at zero, but at a setting used earlier. We labeled this trial +2.
- One single input variable was changed, whose relationship to the output variables was unknown, and the others were not at zero; however, the effect of the other input variable(s) was known from earlier settings. Even so, this combination was not attempted earlier. We labeled this trial +3.
- Everything was maintained in a neutral (zero) position. This trial is especially important for CPS problems with their own internal dynamics. We labeled this +A.
- The value of more than one input variable, whose relationship to the output variables was unknown, was changed at the same time, resulting in no additional information on the system. It was labeled –X.
- The same trial, the slider adjustment, had already been employed earlier within the task, resulting in no additional information on the system. It was labeled −0.
- A new slider adjustment was employed; however, the effect of the manipulated input variables was known from previous settings. This trial offered no additional information on the system and was labeled +0.
Although several input variables were changed by the scenario, it was theoretically possible to count the effect of the input variables on the output variables based on the information from the previous and present settings by using and solving linear equations. It was labeled +4.
An extra code (+5) was employed in the labeling process, but only for the basic input behavior, when the problem solver was able to figure out the structure of the problem based on the information obtained in the last trial used. This labeling has no meaning in the case of the minimal input behavior.
The full, basic and minimal input behavior models as well as the labeling procedure can be employed by analyzing problem solvers' exploration behavior and strategies for problems that are based on minimal complex systems. The user interface can preserve previous input values, and the values are not reset to zero after each exploration input. According to Fischer et al. ( 2012 ), VOTAT strategies are best for identifying causal relations between variables and they maximize the successful strategic behavior in minimal complex systems, such as CPS. By using a VOTAT strategy, the problem solver systematically varies only one input variable, while the others remain unchanged. This way, the effect of the changed variable can be found in the system by monitoring the changes in the output variables. There exist several types of VOTAT strategies based on the different combinations of VOTAT-centered trials +1, +2, and +3. The most obvious systematic strategy is when only one input variable is different from the neutral level in each trial and all the other input variables are systematically maintained at the neutral level. Thus, the strategy is a combination of so-called +1 trials, where it is employed for every input variable. Known as the isolated variation strategy (Müller et al., 2013 ), this strategy has been covered extensively in the literature. It must be noted that the isolated variation strategy is not appropriate to detect multiple dependence effects within the MicroDYN approach. We hypothesize that there are more and less successful input behaviors and strategies. We expect that theoretically effective, non-VOTAT strategies do not work as successfully as VOTAT strategies and that the most effective VOTAT strategy will be the isolated variation strategy.
We will illustrate the labeling and coding process and the course of generating a minimal input behavior out of a basic or full input behavior through the following two examples.
Figure Figure1 1 shows an example with two input variables and two output variables. (The word problem reads as follows: “When you get home in the evening, there is a cat lying on your doorstep. It is exhausted and can barely move. You decide to feed it, and a neighbor gives you two kinds of cat food, Miaow and Catnip. Figure out how Miaow and Catnip impact activity and purring.”). The student who mapped the operation of the system as demonstrated in the figure pressed the Apply button six times in all, using the various settings for the Miaow and Catnip input variables.
In mapping the system, the problem solver kept the value of both the input variables at 0 in the first two steps (making no changes to the base values of the input variables), as a result of which the values of the output variables remained unchanged. In steps 3 and 4, he set the value of the Miaow input variable at 2, while the value of the Catnip variable remained at 0 (the bar chart by the name of each variable shows the history of these settings). Even making this change had no effect on the values of the output variables; that is, the values in each graph by the purring and activity variables are constantly horizontal. In steps 5 and 6, the student left the value of the Miaow input variable at 2, but a value of 2 was added to this for the Catnip input variable. As a result, the values of both output variables (purring and activity) began to grow by the same amount. The coding containing all the information (the full input behavior) for this sequence of steps was as follows: +A, −0, +1, −0, +2, −0. The reason for this is since steps 2, 4, and 6 were repetitions of previous combinations, we coded them as −0. Step 3 involved the purest use of a VOTAT strategy [changing the value of one input variable at a time, while keeping the values of the other input values at a neutral level (+1)], while the trial used in step 5 was also a VOTAT strategy. After all, only the value of one input variable changed compared to step 4. This is therefore not the same trial as we described in step 3 (+2). After step 5, all the necessary information was available to the problem solver. The basic input behavior for the same sequence of steps was +A, +1, +2, since the rest of the steps did not lead the problem solver to acquire unknown information. Independently of the time factor, the minimal input behavior in this case was also +A, +1, +2. The test taker was able to access new information on the operation of the system through these steps. From the point of view of awareness, this +1+2 strategy falls under aware strategy usage, as the +1 and +2 sub-strategies were not applied far apart (excluding the simple repetition of the executed trials next to each other) from each other in time. A good indicator of aware strategy usage is if there is no difference between minimal and basic input behavior.
In the second example (Figure (Figure2), 2 ), we demonstrate the sequence of steps taken in mapping another problem as well as the coding we used. Here the students needed to solve a problem consisting of two input variables and one output variable. The word problem reads as follows: “Your mother has bought two new kinds of fruit drink mix. You want to make yourself a fruit drink with them. Figure out how the green and blue powders impact the sweetness of the drink. Plot your assumptions in the model.” The test taker attempted eight different trials in solving this problem, which were coded as follows: +1, +2, +0, +0, +0, +0, −0, −0. After step 2, the student had access to practically all the information required to plot the causal diagram. (In step 1, the problem solver checked the impact of one scoop of green powder and left the quantity of blue powder at zero. Once mixed, the resultant fruit drink became sweeter. In step 2, the problem solver likewise measured out one scoop of green powder for the drink but also added a scoop of blue powder. The sweetness of the drink changed as much as it had in step 1. After that, the student measured out various quantities of blue and then green powder, and looked at the impact.) The basic input behavior coded from the full input behavior used by the problem solver was +1+2, and the minimal input behavior was +1+1 because the purest VOTAT strategy was used in steps 1 and 6. (Thus, both variables separately confirmed the effects of the blue and the green powder on the sweetness of the drink.) From the point of view of awareness, this +1+1 strategy falls under non-aware strategy usage, as the two applications of the +1 trial occurred far apart from each other in time.
Exploration in phase 1 of the problems based on minimal complex systems (two input variables and one output variable).
Based on students' minimal input behavior we executed latent class analyses. We narrowed the focus to the principle of isolated variation, especially to the extent to which this special strategy was employed in the exploration phase as an indicator of students' ability to proficiently explore the problem environment. We added an extra variable to each of the problems, describing students' exploration behavior based on the following three categories: (1) no isolated variation at all (e.g., isolated variation was employed for none of the input variables – 0 points); (2) partially isolated variation (e.g., isolated variation was employed for some but not all the input variables – 1 point); and (3) fully isolated variation (e.g., isolated variation was employed for all the input variables – 2 points). Thus, depending on the level of optimal exploration strategy used, all the students received new categorical scores based on their input exploration behavior, one for each of the CPS tasks. Let us return to the example provided in Figures Figures1, 1 , ,2. 2 . In the first example, a partially isolated strategy was applied, since the problem solver only used this strategy to test the effect of the Miaow input variables (in trials 3 and 4). In the second example, a full isolated strategy was applied, as the problem solver used this isolated variation strategy for both the input variables during the exploration phase in the first and sixth trials.
The reliability of the test improved when scoring was based on the log data
The reliability of the MicroDYN problems as a measure of knowledge acquisition and knowledge application, the traditional CPS indicators for phases 1 and 2, were acceptable at α = 0.72–0.86 in all grades (Table (Table4). 4 ). After we re-scored the problem solvers' behavior at the beginning of the problem-solving process, coded the log data and assigned new variables for the effectiveness of strategy usage during the exploration phase of the task for each task and person, the overall reliability of the test scores improved. This phenomenon was noted in all grades and in both coding procedures, when the amount of information obtained was examined (Cronbach's α ranged from 0.86 to 0.96) and when the level of optimal exploration strategy used was analyzed (Cronbach's α ranged from 0.83 to 0.98; the answers to the warm-up tasks were excluded from these analyses).
Internal consistencies in scoring the MicroDYN problems: analyses based on both traditional CPS indicators and re-coded log data based on student behavior at the beginning of the problem-solving process.
Use of a theoretically effective strategy does not result in high performance (RQ1)
Use of a theoretically effective strategy did not always result in high performance. The percentage of effective strategy use and high CPS performance varied from 20 to 80%, depending on the complexity of the CPS tasks and the age group. The percentage of theoretically effective strategy use in each cohort increased by 20% for age when problems with the same complexity were compared (Table (Table5) 5 ) and decreased about 20% for the increasing number of input variables in the problems.
Percentage of theoretically effective and non-effective strategy use and high CPS performance.
The percentage of theoretically effective strategy use was the same for the less complex problems in Grades 3–5 and for the most complex tasks in Grades 8–12 (58%). More than 80% of these students solved the problem correctly in the first case, but only 60% had the correct solution in the second case. There was a 50% probability of effective and non-effective strategy use for problems with two input and two output variables in Grades 3–5 and for problems with three input and three output variables in Grades 6–7. In Grades 8–12, the use of a theoretically effective strategy was always higher than 50%, independently of the complexity of the problems (with no internal dynamic). The guessing factor, that is, the ad hoc optimization (use of a theoretically non-effective strategy with the correct solution) also changed, mostly based on the complexity and position of the tasks in the test. The results confirmed our hypothesis that the use of a theoretically effective strategy does not necessary represent the correct solution and that the correct solution does not always represent the use of an even theoretically effective problem-solving strategy.
Not all the VOTAT strategies result in high CPS performance (RQ2)
On average, only 15% of the theoretically effective strategy uses involved non-VOTAT strategies. The isolated variation strategy comprised 45% of the VOTAT strategies employed. It was the only theoretically effective strategy which always resulted in the correct solution to the problem with higher probability independently of problem complexity or the grade of the students. The real advantage of this strategy was most remarkable in the case of the third cohort, where an average of 80% of the students who employed this strategy solved the problems correctly (Figures (Figures3, 3 , ,4 4 ).
Efficacy of the most frequently employed VOTAT strategies on problems with two input variables and one or two output variables in Grades 3–5, 6–7, and 8–12.
Efficacy of the most frequently employed VOTAT strategies on problems with three input variables and one or two output variables in Grades 3–5, 6–7, and 8–12.
The second most frequently employed and successful VOTAT strategy was the +1+2 type or the +1+2+2 type, depending on the number of input variables. In the +1+2 type, only one single input variable was manipulated in the first step, while the other variable remained at a neutral value; in the second step, only the other input variable was changed and the first retained the setting used previously. This proved to be relatively successful on problems with a low level of complexity independently of age, but it generally resulted in a good solution with a low level of probability on more complex problems.
VOTAT strategies of the +1+3 type (in the case of two input variables) and of the +1+1+2 type (in the case of three input variables) were employed even less frequently and with a lower level of efficacy than all the other VOTAT strategies (+1+1+3, +1+2+1, +1+2+2, +1+2+3, +1+3+1, +1+3+2 and +1+3+3 in the case of three input variables) and theoretically effective, non-VOTAT strategies (e.g., +4 in the case of two input variables or +1+4, +4+2 and +4+3 in the case of three input variables). In the following, we provide an example of the +4+2 type, where the MicroDyn problem has three input variables (A, B, and C) and three output variables. In the first trial, the problem solver set the input variables to the following values: 0 (for variable A), 1 (for variable B), and 1 (for variable C); that is, he or she changed two input variables at the same time. In the second trial, he or she changed the value of two input variables at the same time again and applied the following setting: 0 (for variable A), −2 (for variable B), and −1 (for variable C). In the third trial, he set variable A to 1, and left variables B and C unchanged. That is, the problem solver's input behavior can be described with the following trials: –X +4 +2. Based on this strategy, it was possible to map the relationships between the input and output variables without using any VOTAT strategy in the exploration phase.
Aware explorers perform significantly higher on the CPS tasks (RQ3)
We compared the achievement of the aware, isolated strategy users with that of the non-aware explorers (Table (Table6). 6 ). The percentage of high achievers among the non-aware explorers seemed to be almost independent of age, but strongly influenced by the complexity of the problem and the learning effect we noted in the testing procedure (see RQ5). Results for problems with two input variables and one output variable confirmed our previous results, which showed that the probability of providing the correct solution is very high even without aware use of a theoretically effective strategy (60–70%). With more complex problems, the difference between the percentages of aware and non-aware explorers was huge. Generally, 85% of the non-aware explorers failed on the problems, while at least 80% of the aware, isolated strategy users were able to solve the problems correctly.
Percentage of high achievers among aware and non-aware explorers by grade and problem complexity.
Six qualitatively different explorer class profiles can be distinguished at the end of the elementary level and five at the end of the secondary level (RQ4 and RQ5)
In all three cohorts, each of the information theory criteria used (AIC, BIC, and aBIC) indicated a continuous decrease in an increasing number of latent classes. The likelihood ratio statistical test (Lo–Mendell Rubin Adjusted Likelihood Ratio Test) showed the best model fit in Grades 3–5 for the 4-class model, in Grades 6–7 for the 6-class model and in Grades 8–12 for the 5-class model. The entropy-based criterion reached the maximum values for the 2- and 3-class solutions, but it was also high for the best-fitting models based on the information theory and likelihood ratio criteria. Thus, the entropy index for the 4-class model showed that 80% of the 3 rd - to 5 th -graders, 82% of the 6 th - to 7 th -graders and 85% of the 8 th - to 12 th -graders were accurately categorized based on their class membership (Table (Table7 7 ).
Information theory, likelihood ratio and entropy-based fit indices for latent class analyses.
AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; aBIC, adjusted Bayesian Information Criterion; L–M–R test, Lo–Mendell–Rubin Adjusted Likelihood Ratio Test. The best fitting model solution is in italics .
We distinguished four latent classes in the lower grades based on the exploration strategy employed and the level of isolated variation strategy used (Table (Table8): 8 ): 40.5% of the students proved to be non-performing explorers on the basis of their strategic patterns in the CPS environments. They did not use any isolated or partially isolated variation at all; 23.6% of the students were among the low-performing explorers who only rarely employed a fully or partially isolated variation strategy (with 0–20% probability on the less complex problems and 0–5% probability on the more complex problems). 24.7% of the 3 rd - to 5 th -graders were categorized as slow learners who were intermediate performers with regard to the efficiency of the exploration strategy they used on the easiest problems with a slow learning effect, but low-performing explorers on the complex ones. In addition, 11.1% of the students proved to be proficient explorers, who used the isolated or partially isolated variation strategy with 80–100% probability on all the proposed CPS problems.
Relative frequencies and average latent class probabilities across grade levels 3–5, 6–7, and 8–12.
In Grades 6–7, in which achievement proved to be significantly higher on average, 10% fewer students were observed in each of the first two classes (non-performing explorers and low-performing explorers). The percentage of intermediate explorers remained almost the same (26%), and we noted two more classes with the analyses: the class of rapid learners (4.4%) and that of slow learners, who are almost proficient explorers on the easiest problems, employing the fully or partially isolated variation strategy with 60–80% probability, but low-performing explorers on the complex ones (10.3%). The frequency of proficient strategy users was also increased (to 14.2%) compared to students in the lower grades. Finally, there was almost no change detected in the low-performing explorers' classes in Grades 8–12. We did not detect anyone in the class of intermediate explorers; they must have developed further and become (1) rapid learners (7.7%), (2) slow learners with almost high achievement with regard to the exploration strategy they used on the easiest problems, but low achievers on the complex ones (17.6%), or (3) proficient strategy users (26.3%), whose achievement was high both on the simplest and the most complex problems.
Based on these results, the percentage of non- and low explorers, who have very low exploration skills and do not learn during testing, decreased from almost 65 to 50% between the lower and higher primary school levels and then remained constant at the secondary level. There was a slight increase in respect of the percentage of students among the rapid learners. The students in that group used the fully or partially isolated strategy at very low levels at the beginning of the test, but they learned very quickly and detected these effective exploration strategies; thus, by the end of the test, their proficiency level with regard to exploration was equal to the top performers' achievement. However, we were unable to detect the class of rapid learners among 3 rd - to 5 th -graders.
Generally, students' level of exploration expertise with regard to fully and partially isolated variation improved significantly with age ( F = 70.376, p < 0.001). According to our expectations based on the achievement differences among students in Grades 3–5, 6–8 and 9–12, there were also significant differences in the level of expertise in fully or partially isolated strategy use during problem exploration between 3 rd - to 5 th - and 6 th - to 7 th -grade students ( t = −6.833, p < 0.001, d = 0.03) and between 6 th - to 7 th - and 8 th - to 12 th -grade students ( t = −6.993, p < 0.001, d = 0.03).
In this study, we examined 3 rd - to 12 th -grade (aged 9–18) students' problem-solving behavior by means of a logfile analysis to identify qualitatively different exploration strategies. Students' activity in the first phase of the problem-solving process was coded according to a mathematical model that was developed based on strategy effectiveness and then clustered for comparison. Reliability analyses of students' strategy use indicated that strategies used in the knowledge acquisition phase described students' development (ability level) better than traditional quantitative psychometric indicators, including the goodness of the model. The high reliability indices indicate that there are untapped possibilities in analyzing log data. Our analyses of logfiles extracted from a simulation-based assessment of problem solving have expanded the scope of previous studies and made it possible to identify a central component of children's scientific reasoning: the way students understand how scientific experiments can be designed and how causal relationships can be explored by systematically changing the values of (independent) variables and observing their impact on other (target) variables.
In this way, we have introduced a new labeling and scoring method that can be employed in addition to the two scores that have already been used in previous studies. We have found that using this scoring method (based on student strategy use) improves the reliability of the test. Further studies are needed to examine the validity of the scale based on this method and to determine what this scale really measures. We may assume that the general idea of varying the values of the independent variables and connecting them to the resultant changes in the target variable is the essence of scientific reasoning and that the systematic manipulation of variables is related to combinatorial reasoning, while summarizing one's observations and plotting a model is linked to rule induction. Such further studies have to place CPS testing in the context of other cognitive tests and may contribute to efforts to determine the place of CPS in a system of cognitive abilities (see e.g., Wüstenberg et al., 2012 ).
We have found that the use of a theoretically effective strategy does not always result in high performance. This is not surprising, and it confirms research results by de Jong and van Joolingen ( 1998 ), who argue that learners often have trouble interpreting data. As we observed earlier, using a systematic strategy requires combinatorial thinking, while drawing a conclusion from one's observations requires rule induction (inductive reasoning). Students showing systematic strategies but failing to solve the problem may possess combinatorial skills but lack the necessary level of inductive reasoning. It is more difficult to find an explanation for the other direction of discrepancy, when students actually solve the problem without an effective (complete) strategy. Thus, solving the problem does not require the use of a strategy which provides the problem solver with sufficient information about the problem environment to be able to form the correct solution. This finding is similar to results from previous research (e.g., Vollmeyer et al., 1996 ; Greiff et al., 2015 ). Goode and Beckmann ( 2010 ) reported two qualitatively different, but equally effective approaches: knowledge- based and ad hoc control.
In the present study, the contents of the problems were not based on real knowledge, and the causal relationships between the variables were artificial. Content knowledge was therefore no help to the students in filling the gap between the insufficient information acquired from interaction and the successful solution to the problem. We may assume that students guessed intuitively in such a case. Further studies may ascertain how students guess in such situations.
The percentage of success is influenced by the complexity of the CPS tasks, the type of theoretically effective strategy used, the age group and, finally, the degree to which the strategy was consciously employed.
The most frequently employed effective strategies fell within the class of VOTAT strategies. Almost half the VOTAT strategies were of the isolated variation strategy type, which resulted with higher probability in the correct solution independently of the complexity of the problem or the grade of the students. As noted earlier, not all the VOTAT strategies resulted in high CPS performance; moreover, all the other VOTAT strategies proved to be significantly less successful. Some of them worked with relative success on problems with a low level of complexity, but failed with a high level of probability on more complex problems independently of age group. Generally, the advantage of the isolated variation strategy (Wüstenberg et al., 2014 ) compared to the other VOTAT and non-VOTAT, theoretically effective strategies is clearly evident from the outcome. The use of the isolated variation strategy, where students examined the effect of the input variables on the output variables independently, resulted in a good solution with the highest probability and proved to be the most effective VOTAT strategy independently of student age or problem complexity.
Besides the type of strategy used, awareness also played an influential role. Aware VOTAT strategy users proved to be the most successful explorers. They were followed in effectiveness by non-aware VOTAT strategy users and theoretically effective, but non-VOTAT strategy users. They managed to represent the information that they had obtained from the system more effectively and made good decisions in the problem-solving process compared to their peers.
We noted both qualitative and quantitative changes of problem-solving behavior in the age range under examination. Using latent class analyses, we identified six qualitatively different class profiles during compulsory schooling. (1) Non-performing and (2) low-performing students who usually employed no fully or partially isolated variation strategy at all or, if so, then rarely. They basically demonstrated unsystematic exploration behavior. (3) Proficient strategy users who consistently employed optimal exploration strategies from the very first problem as well as the isolated variation strategy and the partially isolated variation, but only seldom. They must have more elaborated schemas available. (4) Slow learners who are intermediate performers on the easiest problems, but low performers on the complex ones or (5) high performers on the easiest problems, but low performers on the complex ones. Most members of this group managed to employ the principle of isolated or partially isolated variation and had an understanding of it, but they were only able to use it on the easiest task and then showed a rapid decline on the more complex CPS problems. They might have been cognitively overloaded by the increasingly difficult problem-solving environments they faced. (6) Rapid learners, a very small group from an educational point of view. These students started out as non-performers in their exploration behavior on the first CPS tasks, showed a rapid learning curve afterwards and began to use the partially isolated variation strategy increasingly and then the fully isolated variation strategy. By the end of the test, they reached the same high level of exploration behavior as the proficient explorers. We observed no so-called intermediate strategy users, i.e., those who used the partially isolated variation strategy almost exclusively on the test. As we expected, class membership increased significantly in the more proficient classes at the higher grade levels due to the effects of cognitive maturation and schooling, but this did not change noticeably in the two lowest-level classes.
Limitations of the study include the low sample size for secondary school students; further, repetition is required for validation. The generalizability of the results is also limited by the effects of semantic embedding (i.e., cover stories and variable labels), that is, the usage of different fictitious cover stories “with the intention of minimizing the uncontrollable effects of prior knowledge, beliefs or suppositions” (Beckmann and Goode, 2017 ). An assumption triggered by semantic contexts has an impact on exploration behavior (e.g., the range of interventions, or strategies employed by the problem solver; Beckmann and Goode, 2014 ), that is, how the problem solver interacts with the system. Limitations also include the characteristics of the interface used. In our view, analyses with regard to VOTAT strategies are only meaningful in systems with an interface where inputs do not automatically reset to zero from one input to the next (Beckmann and Goode, 2017 ). That is, we excluded problem environments from the study where the inputs automatically reset to zero from one input to the next. A further limitation of the generalizability of the results is that we have omitted problems with autonomic changes from the analyses.
The main reason why we have excluded systems that contain autoregressive dependencies from the analyses is that different strategy usage is required on problems which also involve the use of trial +A (according to our coding of sub-strategies), which is not among the effective sub-strategies for problems without autonomic changes. Analyses of students' behavior on problems with autonomic changes will form part of further studies, as well as a refinement of the definition of what makes a problem complex and difficult. We plan to adapt the Person, Task and Situation framework published by Beckmann and Goode ( 2017 ). The role of ad hoc control behavior was excluded from the analyses; further studies are required to ascertain the importance of the repetitive control behavior. Another limitation of the study could be the interpretation of the differences across age group clusters as indicators of development and not as a lack of stability of the model employed.
These results shed new light on and provide a new interpretation of previous analyses of complex problem solving in the MicroDYN approach. They also highlight the importance of explicit enhancement of problem-solving skills and problem-solving strategies as a tool for applying knowledge in a new context during school lessons.
Ethical approval was not required for this study based on national and institutional guidelines. The assessments which provided data for this study were integrated parts of the educational processes of the participating schools. The coding system for the online platform masked students' identity; the data cannot be connected to the students. The results from the no-stakes diagnostic assessments were disclosed only to the participating students (as immediate feedback) and to their teachers. Because of the anonymity and no-stakes testing design of the assessment process, it was not required or possible to request and obtain written informed parental consent from the participants.
Both the authors, GM and BC, certify that they have participated sufficiently in the work to take responsibility for the content, including participation in the concept, design and analysis as well as the writing and final approval of the manuscript. Each author agrees to be accountable for all aspects of the work.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
1 With regard to terminology, please note that different terms are used for the subject at hand (e.g., complex problem solving, dynamic problem solving, interactive problem solving and creative problem solving). In this paper, we use the modifier “complex” (see Csapó and Funke, 2017 ; Dörner and Funke, 2017 ).
Funding. This study was funded by OTKA K115497.
- OECD (2014). PISA 2012 Results: Creative Problem Solving. Students' Skills in Tackling Real-Life Problems (Volume V) . Paris: OECD. [ Google Scholar ]
- Beckmann J. F., Goode N. (2014). The benefit of being näive and knowing it? The unfavourable impact of perceived context familiarity on learning in complex problem solving tasks . Instructional Sci . 42 , 271–290. 10.1007/s11251-013-9280-7 [ CrossRef ] [ Google Scholar ]
- Beckmann J. F., Birney D. P., Goode N. (2017). Beyond psychometrics: the difference between difficult problem solving and complex problem solving . Front. Psychol . 8 :1739. 10.3389/fpsyg.2017.01739 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Beckmann J. F., Goode N. (2017). Missing the wood for the wrong trees: On the difficulty of defining the complexity of complex problem solving scenarios . J. Intell . 5 :2 10.3390/jintelligence5020015 [ CrossRef ] [ Google Scholar ]
- Chen Z., Klahr D. (1999). All other things being equal: acquisition and transfer of the control of variables strategy . Child Dev. 70 , 1098–1120. 10.1111/1467-8624.00081 [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Collins L. M., Lanza S. T. (2010). Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences . New York, NY: Wiley. [ Google Scholar ]
- Csapó B., Ainley J., Bennett R., Latour T., Law N. (2012). Technological issues of computer-based assessment of 21st century skills , in Assessment and Teaching of 21st Century Skills , eds Griffin P., McGaw B., Care E. (New York, NY: Springer; ), 143–230. [ Google Scholar ]
- Csapó B., Funke J. (eds.). (2017). The Nature of Problem Solving. Using Research to Inspire 21st Century Learning. Paris: OECD. [ Google Scholar ]
- Csapó B., Molnár G. (2017). Potential for assessing dynamic problem-solving at the beginning of higher education studies . Front. Psychol. 8 :2022. 10.3389/fpsyg.2017.02022 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Csapó B., Molnár G., Nagy J. (2014). Computer-based assessment of school-readiness and reasoning skills . J. Educ. Psychol. 106 , 639–650. 10.1037/a0035756 [ CrossRef ] [ Google Scholar ]
- de Jong T., van Joolingen W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains . Rev. Educ. Res . 68 , 179–201. 10.3102/00346543068002179 [ CrossRef ] [ Google Scholar ]
- Demetriou A., Efklides A., Platsidou M. (1993). The architecture and dynamics of developing mind: experiential structuralism as a frame for unifying cognitive developmental theories . Monogr. Soc. Res. Child Dev. 58 , 1–205. 10.2307/1166053 [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Dörner D., Funke J. (2017). Complex problem solving: what it is and what it is not . Front. Psychol. 8 :1153 10.3389/fpsyg.2017.01153 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Dziak J. J., Coffman D. L., Lanza S. T., Li R. (2012). Sensitivity and Specificity of Information Criteria . The Pennsylvania State University: The Methodology Center and Department of Statistics. Available online at: https://methodology.psu.edu/media/techreports/12-119.pdf
- Fischer A., Greiff S., Funke J. (2012). The process of solving complex problems . J. Probl. Solving . 4 , 19–42. 10.7771/1932-6246.1118 [ CrossRef ] [ Google Scholar ]
- Funke J. (2001). Dynamic systems as tools for analysing human judgement . Think. Reason. 7 , 69–89. 10.1080/13546780042000046 [ CrossRef ] [ Google Scholar ]
- Funke J. (2010). Complex problem solving: a case for complex cognition? Cogn. Process. 11 , 133–142. 10.1007/s10339-009-0345-0 [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Funke J. (2014). Analysis of minimal complex systems and complex problem solving require different forms of causal cognition . Front. Psychol. 5 :739. 10.3389/fpsyg.2014.00739 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Galotti K. M. (2011). Cognitive Development . Thousand Oaks, CA: SAGE. [ Google Scholar ]
- Gardner P. H., Berry D. C. (1995). The effect of different forms of advice on the control of a simulated complex system . Appl. Cogn. Psychol. 9 :7 10.1002/acp.2350090706 [ CrossRef ] [ Google Scholar ]
- Goode N., Beckmann J. F. (2010). You need to know: there is a causal relationship between structural knowledge and control performance in complex problem solving tasks . Intelligence 38 , 345–352. 10.1016/j.intell.2010.01.001 [ CrossRef ] [ Google Scholar ]
- Greiff S., Funke J. (2010). Systematische Erforschung komplexer Problemlösefähigkeit anhand minimal komplexer Systeme . Zeitschrift für Pädagogik 56 , 216–227. [ Google Scholar ]
- Greiff S., Funke J. (2017). Interactive problem solving: exploring the potential of minimal complex systems , in The Nature of Problem Solving. Using Research to Inspire 21st Century Learning , eds Csapó B., Funke J. (Paris: OECD; ), 93–105. [ Google Scholar ]
- Greiff S., Wüstenberg S., Avvisati F. (2015). Computer-generated log-file analyses as a window into students' minds? A showcase study based on the PISA 2012 assessment of problem solving . Computers Educ . 91 , 92–105. 10.1016/j.compedu.2015.10.018 [ CrossRef ] [ Google Scholar ]
- Greiff S., Wüstenberg S., Csapó B., Demetriou A., Hautamäki H., Graesser A. C., et al. (2014). Domain-general problem solving skills and education in the 21st century . Educ. Res. Rev . 13 , 74–83. 10.1016/j.edurev.2014.10.002 [ CrossRef ] [ Google Scholar ]
- Greiff S., Wüstenberg S., Funke J. (2012). Dynamic problem solving: a new assessment perspective . Appl. Psychol. Meas. 36 , 189–213. 10.1177/0146621612439620 [ CrossRef ] [ Google Scholar ]
- Greiff S., Wüstenberg S., Goetz T., Vainikainen M.-P., Hautamäki J., Bornstein M. H. (2015). A longitudinal study of higher-order thinking skills: working memory and fluid reasoning in childhood enhance complex problem solving in adolescence . Front. Psychol. 6 :1060. 10.3389/fpsyg.2015.01060 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Greiff S., Wüstenberg S., Molnár G., Fischer A., Funke J., Csapó B. (2013). Complex problem solving in educational contexts – Something beyond g: Concept, assessment, measurement invariance, and construct validity . J. Educ. Psychol . 105 , 364–379. 10.1037/a0031856 [ CrossRef ] [ Google Scholar ]
- Griffin P., McGaw B., Care E. (2012). Assessment and Teaching of 21st Century Skills . Dordrecht: Springer. [ Google Scholar ]
- Inhelder B., Piaget J. (1958). The Growth of Logical Thinking from Childhood to Adolescence . New York, NY: Basic Books. [ Google Scholar ]
- Klahr D., Dunbar K. (1988). Dual space search during scientific reasoning . Cogn. Sci. 12 , 1–48. 10.1207/s15516709cog1201_1 [ CrossRef ] [ Google Scholar ]
- Klahr D., Triona L. M., Williams C. (2007). Hands on what? The relative effectiveness of physical versus virtual materials in an engineering design project by middle school children . J. Res. Sci. Teaching 44 , 183–203. 10.1002/tea.20152 [ CrossRef ] [ Google Scholar ]
- Kröner S., Plass J. L., Leutner D. (2005). Intelligence assessment with computer simulations . Intelligence 33 , 347–368. 10.1016/j.intell.2005.03.002 [ CrossRef ] [ Google Scholar ]
- Kuhn D., Iordanou K., Pease M., Wirkala C. (2008). Beyond control of variables: what needs to develop to achieve skilled scientific thinking? Cogn. Dev . 23 , 435–451. 10.1016/j.cogdev.2008.09.006 [ CrossRef ] [ Google Scholar ]
- Lo Y., Mendell N. R., Rubin D. B. (2001). Testing the number of components in a normal mixture . Biometrika 88 , 767–778. 10.1093/biomet/88.3.767 [ CrossRef ] [ Google Scholar ]
- Lotz C., Scherer R., Greiff S., Sparfeldt J. R. (2017). Intelligence in action – Effective strategic behaviors while solving complex problems . Intelligence 64 , 98–112. 10.1016/j.intell.2017.08.002 [ CrossRef ] [ Google Scholar ]
- Mayer R. E., Wittrock M. C. (1996). Problem-solving transfer in Handbook of Educational Psychology , eds Berliner D. C., Calfee R. C. (New York, NY; London: Routledge; ), 47–62. [ Google Scholar ]
- Molnár G., Greiff S., Csapó B. (2013). Inductive reasoning, domain specific and complex problem solving: relations and development . Think. Skills Creat. 9 , 35–45. 10.1016/j.tsc.2013.03.002 [ CrossRef ] [ Google Scholar ]
- Molnár G., Greiff S., Wüstenberg S., Fischer A. (2017). Empirical study of computer-based assessment of domain-general complex problem-solving skills , in The Nature of Problem Solving. Using Research to Inspire 21st Century Learning , eds Csapó B., Funke J. (Paris: OECD; ), 125–140. [ Google Scholar ]
- Müller J. C., Kretzschmar A., Greiff S. (2013). Exploring exploration: Inquiries into exploration behavior in complex problem solving assessment , in Proceedings of the 6th International Conference on Educational Data Mining , eds D'Mello S. K., Calvo R. A., Olney A. 336–337. Available online at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.666.6664&rep=rep1&type=pdf [ Google Scholar ]
- Muthén L. K., Muthén B. O. (2012). Mplus User's Guide, 7th Edn . Los Angeles, CA: Muthén and Muthén. [ Google Scholar ]
- Osman M., Speekenbrink M. (2011). Cue utilization and strategy application in stable and unstable dynamic environments . Cogn. Syst. Res . 12 , 355–364. 10.1016/j.cogsys.2010.12.004 [ CrossRef ] [ Google Scholar ]
- Schoppek W., Fischer A. (2017). Common process demands of two complex dynamic control tasks: transfer is mediated by comprehensive strategies . Front. Psychol . 8 :2145. 10.3389/fpsyg.2017.02145 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Siegler R. S. (1999). Strategic development . Trends Cogn. Sci. 3 , 430–435. [ PubMed ] [ Google Scholar ]
- Sonnleiter P., Keller U., Martin R., Latour T., Brunner M. (2017). Assessing complex problem solving in the classroom: meeting challenges and opportunities , in The Nature of Problem Solving. Using Research to Inspire 21st Century Learning , eds Csapó B., Funke J. (Paris: OECD; ), 159–174. [ Google Scholar ]
- Sweller J. (1994). Cognitive load theory, learning difficulty, and instructional design . Learn. Instruct . 4 , 295–312. 10.1016/0959-4752(94)90003-5 [ CrossRef ] [ Google Scholar ]
- Tein J. Y., Coxe S., Cham H. (2013). Statistical power to detect the correct number of classes in latent profile analysis . Struct. Equ. Modeling. 20 , 640–657. 10.1080/10705511.2013.824781 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
- Tóth K., Rölke H., Goldhammer F., Barkow I. (2017). Educational process mining: New possibilities for understanding students' problem-solving skills , in The Nature of Problem Solving. Using Research to Inspire 21st Century Learning , eds. Csapó B., Funke J. (Paris: OECD; ), 193–210. [ Google Scholar ]
- Tschirgi J. E. (1980). Sensible reasoning: a hypothesis about hypotheses . Child Dev. 51 , 1–10. 10.2307/1129583 [ CrossRef ] [ Google Scholar ]
- Vollmeyer R., Burns B. D., Holyoak K. J. (1996). The impact of goal specificity on strategy use and the acquisition of problem structure . Cogn. Sci. 20 , 75–100. 10.1207/s15516709cog2001_3 [ CrossRef ] [ Google Scholar ]
- Wüstenberg S., Greiff S., Funke J. (2012). Complex problem solving. More than reasoning? Intelligence 40 , 1–14. 10.1016/j.intell.2011.11.003 [ CrossRef ] [ Google Scholar ]
- Wüstenberg S., Stadler M., Hautamäki J., Greiff S. (2014). The role of strategy knowledge for the application of strategies in complex problem solving tasks . Technol. Knowledge Learn. 19 , 127–146. 10.1007/s10758-014-9222-8 [ CrossRef ] [ Google Scholar ]
- Zoanetti N., Griffin P. (2017). Log-file data as indicators for problem-solving processes , in The Nature of Problem Solving. Using Research to Inspire 21st Century Learning , eds Csapó B., Funke J. (Paris: OECD; ), 177–191. [ Google Scholar ]
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- Published: 11 January 2023
The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature
- Enwei Xu ORCID: orcid.org/0000-0001-6424-8169 1 ,
- Wei Wang 1 &
- Qingxia Wang 1
Humanities and Social Sciences Communications volume 10 , Article number: 16 ( 2023 ) Cite this article
- Science, technology and society
Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z = 12.78, P < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z = 7.62, P < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z = 11.55, P < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2 = 7.20, P < 0.05), intervention duration (chi 2 = 12.18, P < 0.01), subject area (chi 2 = 13.36, P < 0.05), group size (chi 2 = 8.77, P < 0.05), and learning scaffold (chi 2 = 9.03, P < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.
Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.
Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).
Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.
The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).
This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:
What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?
How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?
This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.
Data sources and search strategies
There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.
This flowchart shows the number of records identified, included and excluded in the article.
First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.
Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.
Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.
Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.
Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:
The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.
The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.
The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.
The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.
The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.
Data coding design
In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.
The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.
The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.
The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).
Procedure for extracting and coding data
According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.
Publication bias test
When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.
This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.
To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2 ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.
The analysis of the overall effect size
This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z = 12.78, P < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.
This forest plot shows the analysis result of the overall effect size across 36 studies.
In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2 = 7.95, P < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z = 7.62, P < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z = 11.55, P < 0.01, 95% CI [0.58, 0.82]).
The analysis of moderator effect size
The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2 = 86%, z = 12.78, P < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2 = 13.36, P < 0.05), group size (chi 2 = 8.77, P < 0.05), intervention duration (chi 2 = 12.18, P < 0.01), learning scaffold (chi 2 = 9.03, P < 0.01), and teaching type (chi 2 = 7.20, P < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2 = 3.15, P = 0.21 > 0.05, and chi 2 = 0.08, P = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:
Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2 = 3.15, P = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P < 0.01), then higher education (ES = 0.78, P < 0.01), and middle school (ES = 0.73, P < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.
Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2 = 7.20, P < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P < 0.01), integrated courses (ES = 0.81, P < 0.01), and independent courses (ES = 0.27, P < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.
Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2 = 12.18, P < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.
Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2 = 9.03, P < 0.01). The resource-supported learning scaffold (ES = 0.69, P < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.
Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2 = 8.77, P < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.
Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2 = 0.08, P = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.
Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2 = 13.36, P < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P < 0.01), followed by science (ES = 1.25, P < 0.01) and medical science (ES = 0.87, P < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.
The effectiveness of collaborative problem solving with regard to teaching critical thinking
According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.
Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.
The moderating effects of collaborative problem solving with regard to teaching critical thinking
In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.
In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.
Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.
With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).
In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.
With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.
With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).
With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.
With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).
Suggestions for critical thinking teaching
Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.
First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.
Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.
Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.
Implications and limitations
There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.
The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:
Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z = 12.78, P < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z = 7.62, P < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z = 11.55, P < 0.01, 95% CI [0.58, 0.82]).
As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2 = 7.20, P < 0.05), intervention duration (chi 2 = 12.18, P < 0.01), subject area (chi 2 = 13.36, P < 0.05), group size (chi 2 = 8.77, P < 0.05), and learning scaffold (chi 2 = 9.03, P < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2 = 3.15, P = 0.21 > 0.05) and measuring tools (chi 2 = 0.08, P = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.
All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .
Bensley DA, Spero RA (2014) Improving critical thinking skills and meta-cognitive monitoring through direct infusion. Think Skills Creat 12:55–68. https://doi.org/10.1016/j.tsc.2014.02.001
Article Google Scholar
Castle A (2009) Defining and assessing critical thinking skills for student radiographers. Radiography 15(1):70–76. https://doi.org/10.1016/j.radi.2007.10.007
Chen XD (2013) An empirical study on the influence of PBL teaching model on critical thinking ability of non-English majors. J PLA Foreign Lang College 36 (04):68–72
Cohen A (1992) Antecedents of organizational commitment across occupational groups: a meta-analysis. J Organ Behav. https://doi.org/10.1002/job.4030130602
Cooper H (2010) Research synthesis and meta-analysis: a step-by-step approach, 4th edn. Sage, London, England
Cindy HS (2004) Problem-based learning: what and how do students learn? Educ Psychol Rev 51(1):31–39
Duch BJ, Gron SD, Allen DE (2001) The power of problem-based learning: a practical “how to” for teaching undergraduate courses in any discipline. Stylus Educ Sci 2:190–198
Ennis RH (1989) Critical thinking and subject specificity: clarification and needed research. Educ Res 18(3):4–10. https://doi.org/10.3102/0013189x018003004
Facione PA (1990) Critical thinking: a statement of expert consensus for purposes of educational assessment and instruction. Research findings and recommendations. Eric document reproduction service. https://eric.ed.gov/?id=ed315423
Facione PA, Facione NC (1992) The California Critical Thinking Dispositions Inventory (CCTDI) and the CCTDI test manual. California Academic Press, Millbrae, CA
Forawi SA (2016) Standard-based science education and critical thinking. Think Skills Creat 20:52–62. https://doi.org/10.1016/j.tsc.2016.02.005
Halpern DF (2001) Assessing the effectiveness of critical thinking instruction. J Gen Educ 50(4):270–286. https://doi.org/10.2307/27797889
Hu WP, Liu J (2015) Cultivation of pupils’ thinking ability: a five-year follow-up study. Psychol Behav Res 13(05):648–654. https://doi.org/10.3969/j.issn.1672-0628.2015.05.010
Huber K (2016) Does college teach critical thinking? A meta-analysis. Rev Educ Res 86(2):431–468. https://doi.org/10.3102/0034654315605917
Kek MYCA, Huijser H (2011) The power of problem-based learning in developing critical thinking skills: preparing students for tomorrow’s digital futures in today’s classrooms. High Educ Res Dev 30(3):329–341. https://doi.org/10.1080/07294360.2010.501074
Kuncel NR (2011) Measurement and meaning of critical thinking (Research report for the NRC 21st Century Skills Workshop). National Research Council, Washington, DC
Kyndt E, Raes E, Lismont B, Timmers F, Cascallar E, Dochy F (2013) A meta-analysis of the effects of face-to-face cooperative learning. Do recent studies falsify or verify earlier findings? Educ Res Rev 10(2):133–149. https://doi.org/10.1016/j.edurev.2013.02.002
Leng J, Lu XX (2020) Is critical thinking really teachable?—A meta-analysis based on 79 experimental or quasi experimental studies. Open Educ Res 26(06):110–118. https://doi.org/10.13966/j.cnki.kfjyyj.2020.06.011
Liang YZ, Zhu K, Zhao CL (2017) An empirical study on the depth of interaction promoted by collaborative problem solving learning activities. J E-educ Res 38(10):87–92. https://doi.org/10.13811/j.cnki.eer.2017.10.014
Lipsey M, Wilson D (2001) Practical meta-analysis. International Educational and Professional, London, pp. 92–160
Liu Z, Wu W, Jiang Q (2020) A study on the influence of problem based learning on college students’ critical thinking-based on a meta-analysis of 31 studies. Explor High Educ 03:43–49
Morris SB (2008) Estimating effect sizes from pretest-posttest-control group designs. Organ Res Methods 11(2):364–386. https://doi.org/10.1177/1094428106291059
Article ADS Google Scholar
Mulnix JW (2012) Thinking critically about critical thinking. Educ Philos Theory 44(5):464–479. https://doi.org/10.1111/j.1469-5812.2010.00673.x
Naber J, Wyatt TH (2014) The effect of reflective writing interventions on the critical thinking skills and dispositions of baccalaureate nursing students. Nurse Educ Today 34(1):67–72. https://doi.org/10.1016/j.nedt.2013.04.002
National Research Council (2012) Education for life and work: developing transferable knowledge and skills in the 21st century. The National Academies Press, Washington, DC
Niu L, Behar HLS, Garvan CW (2013) Do instructional interventions influence college students’ critical thinking skills? A meta-analysis. Educ Res Rev 9(12):114–128. https://doi.org/10.1016/j.edurev.2012.12.002
Peng ZM, Deng L (2017) Towards the core of education reform: cultivating critical thinking skills as the core of skills in the 21st century. Res Educ Dev 24:57–63. https://doi.org/10.14121/j.cnki.1008-3855.2017.24.011
Reiser BJ (2004) Scaffolding complex learning: the mechanisms of structuring and problematizing student work. J Learn Sci 13(3):273–304. https://doi.org/10.1207/s15327809jls1303_2
Ruggiero VR (2012) The art of thinking: a guide to critical and creative thought, 4th edn. Harper Collins College Publishers, New York
Schellens T, Valcke M (2006) Fostering knowledge construction in university students through asynchronous discussion groups. Comput Educ 46(4):349–370. https://doi.org/10.1016/j.compedu.2004.07.010
Sendag S, Odabasi HF (2009) Effects of an online problem based learning course on content knowledge acquisition and critical thinking skills. Comput Educ 53(1):132–141. https://doi.org/10.1016/j.compedu.2009.01.008
Sison R (2008) Investigating Pair Programming in a Software Engineering Course in an Asian Setting. 2008 15th Asia-Pacific Software Engineering Conference, pp. 325–331. https://doi.org/10.1109/APSEC.2008.61
Simpson E, Courtney M (2002) Critical thinking in nursing education: literature review. Mary Courtney 8(2):89–98
Stewart L, Tierney J, Burdett S (2006) Do systematic reviews based on individual patient data offer a means of circumventing biases associated with trial publications? Publication bias in meta-analysis. John Wiley and Sons Inc, New York, pp. 261–286
Tiwari A, Lai P, So M, Yuen K (2010) A comparison of the effects of problem-based learning and lecturing on the development of students’ critical thinking. Med Educ 40(6):547–554. https://doi.org/10.1111/j.1365-2929.2006.02481.x
Wood D, Bruner JS, Ross G (2006) The role of tutoring in problem solving. J Child Psychol Psychiatry 17(2):89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
Wei T, Hong S (2022) The meaning and realization of teachable critical thinking. Educ Theory Practice 10:51–57
Xu EW, Wang W, Wang QX (2022) A meta-analysis of the effectiveness of programming teaching in promoting K-12 students’ computational thinking. Educ Inf Technol. https://doi.org/10.1007/s10639-022-11445-2
Yang YC, Newby T, Bill R (2008) Facilitating interactions through structured web-based bulletin boards: a quasi-experimental study on promoting learners’ critical thinking skills. Comput Educ 50(4):1572–1585. https://doi.org/10.1016/j.compedu.2007.04.006
Yore LD, Pimm D, Tuan HL (2007) The literacy component of mathematical and scientific literacy. Int J Sci Math Educ 5(4):559–589. https://doi.org/10.1007/s10763-007-9089-4
Zhang T, Zhang S, Gao QQ, Wang JH (2022) Research on the development of learners’ critical thinking in online peer review. Audio Visual Educ Res 6:53–60. https://doi.org/10.13811/j.cnki.eer.2022.06.08
This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).
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Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1
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DOI : https://doi.org/10.1057/s41599-023-01508-1
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Introduction to Problem Solving Skills
What is problem solving and why is it important.
The ability to solve problems is a basic life skill and is essential to our day-to-day lives, at home, at school, and at work. We solve problems every day without really thinking about how we solve them. For example: it’s raining and you need to go to the store. What do you do? There are lots of possible solutions. Take your umbrella and walk. If you don't want to get wet, you can drive, or take the bus. You might decide to call a friend for a ride, or you might decide to go to the store another day. There is no right way to solve this problem and different people will solve it differently.
Problem solving is the process of identifying a problem, developing possible solution paths, and taking the appropriate course of action.
Why is problem solving important? Good problem solving skills empower you not only in your personal life but are critical in your professional life. In the current fast-changing global economy, employers often identify everyday problem solving as crucial to the success of their organizations. For employees, problem solving can be used to develop practical and creative solutions, and to show independence and initiative to employers.
Throughout this case study you will be asked to jot down your thoughts in idea logs. These idea logs are used for reflection on concepts and for answering short questions. When you click on the "Next" button, your responses will be saved for that page. If you happen to close the webpage, you will lose your work on the page you were on, but previous pages will be saved. At the end of the case study, click on the "Finish and Export to PDF" button to acknowledge completion of the case study and receive a PDF document of your idea logs.
What Does Problem Solving Look Like?
The ability to solve problems is a skill, and just like any other skill, the more you practice, the better you get. So how exactly do you practice problem solving? Learning about different problem solving strategies and when to use them will give you a good start. Problem solving is a process. Most strategies provide steps that help you identify the problem and choose the best solution. There are two basic types of strategies: algorithmic and heuristic.
Algorithmic strategies are traditional step-by-step guides to solving problems. They are great for solving math problems (in algebra: multiply and divide, then add or subtract) or for helping us remember the correct order of things (a mnemonic such as “Spring Forward, Fall Back” to remember which way the clock changes for daylight saving time, or “Righty Tighty, Lefty Loosey” to remember what direction to turn bolts and screws). Algorithms are best when there is a single path to the correct solution.
But what do you do when there is no single solution for your problem? Heuristic methods are general guides used to identify possible solutions. A popular one that is easy to remember is IDEAL [ Bransford & Stein, 1993 ] :
- I dentify the problem
- D efine the context of the problem
- E xplore possible strategies
- A ct on best solution
IDEAL is just one problem solving strategy. Building a toolbox of problem solving strategies will improve your problem solving skills. With practice, you will be able to recognize and use multiple strategies to solve complex problems.
Watch the video
What is the best way to get a peanut out of a tube that cannot be moved? Watch a chimpanzee solve this problem in the video below [ Geert Stienissen, 2010 ].
Describe the series of steps you think the chimpanzee used to solve this problem.
- [Page 2: What does Problem Solving Look Like?] Describe the series of steps you think the chimpanzee used to solve this problem.
Think of an everyday problem you've encountered recently and describe your steps for solving it.
- [Page 2: What does Problem Solving Look Like?] Think of an everyday problem you've encountered recently and describe your steps for solving it.
Developing Problem Solving Processes
Problem solving is a process that uses steps to solve problems. But what does that really mean? Let's break it down and start building our toolbox of problem solving strategies.
What is the first step of solving any problem? The first step is to recognize that there is a problem and identify the right cause of the problem. This may sound obvious, but similar problems can arise from different events, and the real issue may not always be apparent. To really solve the problem, it's important to find out what started it all. This is called identifying the root cause .
Example: You and your classmates have been working long hours on a project in the school's workshop. The next afternoon, you try to use your student ID card to access the workshop, but discover that your magnetic strip has been demagnetized. Since the card was a couple of years old, you chalk it up to wear and tear and get a new ID card. Later that same week you learn that several of your classmates had the same problem! After a little investigation, you discover that a strong magnet was stored underneath a workbench in the workshop. The magnet was the root cause of the demagnetized student ID cards.
The best way to identify the root cause of the problem is to ask questions and gather information. If you have a vague problem, investigating facts is more productive than guessing a solution. Ask yourself questions about the problem. What do you know about the problem? What do you not know? When was the last time it worked correctly? What has changed since then? Can you diagram the process into separate steps? Where in the process is the problem occurring? Be curious, ask questions, gather facts, and make logical deductions rather than assumptions.
Watch Adam Savage from Mythbusters, describe his problem solving process [ ForaTv, 2010 ]. As you watch this section of the video, try to identify the questions he asks and the different strategies he uses.
Adam Savage shared many of his problem solving processes. List the ones you think are the five most important. Your list may be different from other people in your class—that's ok!
- [Page 3: Developing Problem Solving Processes] Adam Savage shared many of his problem solving processes. List the ones you think are the five most important.
“The ability to ask the right question is more than half the battle of finding the answer.” — Thomas J. Watson , founder of IBM
Voices From the Field: Solving Problems
In manufacturing facilities and machine shops, everyone on the floor is expected to know how to identify problems and find solutions. Today's employers look for the following skills in new employees: to analyze a problem logically, formulate a solution, and effectively communicate with others.
In this video, industry professionals share their own problem solving processes, the problem solving expectations of their employees, and an example of how a problem was solved.
Meet the Partners:
- Taconic High School in Pittsfield, Massachusetts, is a comprehensive, fully accredited high school with special programs in Health Technology, Manufacturing Technology, and Work-Based Learning.
- Berkshire Community College in Pittsfield, Massachusetts, prepares its students with applied manufacturing technical skills, providing hands-on experience at industrial laboratories and manufacturing facilities, and instructing them in current technologies.
- H.C. Starck in Newton, Massachusetts, specializes in processing and manufacturing technology metals, such as tungsten, niobium, and tantalum. In almost 100 years of experience, they hold over 900 patents, and continue to innovate and develop new products.
- Nypro Healthcare in Devens, Massachusetts, specializes in precision injection-molded healthcare products. They are committed to good manufacturing processes including lean manufacturing and process validation.
Now that you have a couple problem solving strategies in your toolbox, let's practice. In this exercise, you are given a scenario and you will be asked to decide what steps you would take to identify and solve the problem.
Scenario: You are a new employee and have just finished your training. As your first project, you have been assigned the milling of several additional components for a regular customer. Together, you and your trainer, Bill, set up for the first run. Checking your paperwork, you gather the tools and materials on the list. As you are mounting the materials on the table, you notice that you didn't grab everything and hurriedly grab a few more items from one of the bins. Once the material is secured on the CNC table, you load tools into the tool carousel in the order listed on the tool list and set the fixture offsets.
Bill tells you that since this is a rerun of a job several weeks ago, the CAD/CAM model has already been converted to CNC G-code. Bill helps you download the code to the CNC machine. He gives you the go-ahead and leaves to check on another employee. You decide to start your first run.
What problems did you observe in the video?
- [Page 5: Making Decisions] What problems did you observe in the video?
- What do you do next?
- Try to fix it yourself.
- Ask your trainer for help.
As you are cleaning up, you think about what happened and wonder why it happened. You try to create a mental picture of what happened. You are not exactly sure what the end mill hit, but it looked like it might have hit the dowel pin. You wonder if you grabbed the correct dowel pins from the bins earlier.
You can think of two possible next steps. You can recheck the dowel pin length to make sure it is the correct length, or do a dry run using the CNC single step or single block function with the spindle empty to determine what actually happened.
- Check the dowel pins.
- Use the single step/single block function to determine what happened.
You notice that your trainer, Bill, is still on the floor and decide to ask him for help. You describe the problem to him. Bill asks if you know what the end mill ran into. You explain that you are not sure but you think it was the dowel pin. Bill reminds you that it is important to understand what happened so you can fix the correct problem. He suggests that you start all over again and begin with a dry run using the single step/single block function, with the spindle empty, to determine what it hit. Or, since it happened at the end, he mentions that you can also check the G-code to make sure the Z-axis is raised before returning to the home position.
- Run the single step/single block function.
- Edit the G-code to raise the Z-axis.
You finish cleaning up and check the CNC for any damage. Luckily, everything looks good. You check your paperwork and gather the components and materials again. You look at the dowel pins you used earlier, and discover that they are not the right length. As you go to grab the correct dowel pins, you have to search though several bins. For the first time, you are aware of the mess - it looks like the dowel pins and other items have not been put into the correctly labeled bins. You spend 30 minutes straightening up the bins and looking for the correct dowel pins.
Finally finding them, you finish setting up. You load tools into the tool carousel in the order listed on the tool list and set the fixture offsets. Just to make sure, you use the CNC single step/single block function, to do a dry run of the part. Everything looks good! You are ready to create your first part. The first component is done, and, as you admire your success, you notice that the part feels hotter than it should.
You wonder why? You go over the steps of the process to mentally figure out what could be causing the residual heat. You wonder if there is a problem with the CNC's coolant system or if the problem is in the G-code.
- Look at the G-code.
After thinking about the problem, you decide that maybe there's something wrong with the setup. First, you clean up the damaged materials and remove the broken tool. You check the CNC machine carefully for any damage. Luckily, everything looks good. It is time to start over again from the beginning.
You again check your paperwork and gather the tools and materials on the setup sheet. After securing the new materials, you use the CNC single step/single block function with the spindle empty, to do a dry run of the part. You watch carefully to see if you can figure out what happened. It looks to you like the spindle barely misses hitting the dowel pin. You determine that the end mill was broken when it hit the dowel pin while returning to the start position.
After conducting a dry run using the single step/single block function, you determine that the end mill was damaged when it hit the dowel pin on its return to the home position. You discuss your options with Bill. Together, you decide the best thing to do would be to edit the G-code and raise the Z-axis before returning to home. You open the CNC control program and edit the G-code. Just to make sure, you use the CNC single step/single block function, to do another dry run of the part. You are ready to create your first part. It works. You first part is completed. Only four more to go.
As you are cleaning up, you notice that the components are hotter than you expect and the end mill looks more worn than it should be. It dawns on you that while you were milling the component, the coolant didn't turn on. You wonder if it is a software problem in the G-code or hardware problem with the CNC machine.
It's the end of the day and you decide to finish the rest of the components in the morning.
- You decide to look at the G-code in the morning.
- You leave a note on the machine, just in case.
You decide that the best thing to do would be to edit the G-code and raise the Z-axis of the spindle before it returns to home. You open the CNC control program and edit the G-code.
While editing the G-code to raise the Z-axis, you notice that the coolant is turned off at the beginning of the code and at the end of the code. The coolant command error caught your attention because your coworker, Mark, mentioned having a similar issue during lunch. You change the coolant command to turn the mist on.
- You decide to talk with your supervisor.
- You discuss what happened with a coworker over lunch.
As you reflect on the residual heat problem, you think about the machining process and the factors that could have caused the issue. You try to think of anything and everything that could be causing the issue. Are you using the correct tool for the specified material? Are you using the specified material? Is it running at the correct speed? Is there enough coolant? Are there chips getting in the way?
Wait, was the coolant turned on? As you replay what happened in your mind, you wonder why the coolant wasn't turned on. You decide to look at the G-code to find out what is going on.
From the milling machine computer, you open the CNC G-code. You notice that there are no coolant commands. You add them in and on the next run, the coolant mist turns on and the residual heat issues is gone. Now, its on to creating the rest of the parts.
Have you ever used brainstorming to solve a problem? Chances are, you've probably have, even if you didn't realize it.
You notice that your trainer, Bill, is on the floor and decide to ask him for help. You describe the problem with the end mill breaking, and how you discovered that items are not being returned to the correctly labeled bins. You think this caused you to grab the incorrect length dowel pins on your first run. You have sorted the bins and hope that the mess problem is fixed. You then go on to tell Bill about the residual heat issue with the completed part.
Together, you go to the milling machine. Bill shows you how to check the oil and coolant levels. Everything looks good at the machine level. Next, on the CNC computer, you open the CNC G-code. While looking at the code, Bill points out that there are no coolant commands. Bill adds them in and when you rerun the program, it works.
Bill is glad you mentioned the problem to him. You are the third worker to mention G-code issues over the last week. You noticed the coolant problems in your G-code, John noticed a Z-axis issue in his G-code, and Sam had issues with both the Z-axis and the coolant. Chances are, there is a bigger problem and Bill will need to investigate the root cause .
Talking with Bill, you discuss the best way to fix the problem. Bill suggests editing the G-code to raise the Z-axis of the spindle before it returns to its home position. You open the CNC control program and edit the G-code. Following the setup sheet, you re-setup the job and use the CNC single step/single block function, to do another dry run of the part. Everything looks good, so you run the job again and create the first part. It works. Since you need four of each component, you move on to creating the rest of them before cleaning up and leaving for the day.
It's a new day and you have new components to create. As you are setting up, you go in search of some short dowel pins. You discover that the bins are a mess and components have not been put away in the correctly labeled bins. You wonder if this was the cause of yesterday's problem. As you reorganize the bins and straighten up the mess, you decide to mention the mess issue to Bill in your afternoon meeting.
You describe the bin mess and using the incorrect length dowels to Bill. He is glad you mentioned the problem to him. You are not the first person to mention similar issues with tools and parts not being put away correctly. Chances are there is a bigger safety issue here that needs to be addressed in the next staff meeting.
In any workplace, following proper safety and cleanup procedures is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly and sometimes dangerous equipment. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money.
You now know that the end mill was damaged when it hit the dowel pin. It seems to you that the easiest thing to do would be to edit the G-code and raise the Z-axis position of the spindle before it returns to the home position. You open the CNC control program and edit the G-code, raising the Z-axis. Starting over, you follow the setup sheet and re-setup the job. This time, you use the CNC single step/single block function, to do another dry run of the part. Everything looks good, so you run the job again and create the first part.
At the end of the day, you are reviewing your progress with your trainer, Bill. After you describe the day's events, he reminds you to always think about safety and the importance of following work procedures. He decides to bring the issue up in the next morning meeting as a reminder to everyone.
In any workplace, following proper procedures (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. One tool to improve communication is the morning meeting or huddle.
The next morning, you check the G-code to determine what is wrong with the coolant. You notice that the coolant is turned off at the beginning of the code and also at the end of the code. This is strange. You change the G-code to turn the coolant on at the beginning of the run and off at the end. This works and you create the rest of the parts.
Throughout the day, you keep wondering what caused the G-code error. At lunch, you mention the G-code error to your coworker, John. John is not surprised. He said that he encountered a similar problem earlier this week. You decide to talk with your supervisor the next time you see him.
You are in luck. You see your supervisor by the door getting ready to leave. You hurry over to talk with him. You start off by telling him about how you asked Bill for help. Then you tell him there was a problem and the end mill was damaged. You describe the coolant problem in the G-code. Oh, and by the way, John has seen a similar problem before.
Your supervisor doesn't seem overly concerned, errors happen. He tells you "Good job, I am glad you were able to fix the issue." You are not sure whether your supervisor understood your explanation of what happened or that it had happened before.
The challenge of communicating in the workplace is learning how to share your ideas and concerns. If you need to tell your supervisor that something is not going well, it is important to remember that timing, preparation, and attitude are extremely important.
It is the end of your shift, but you want to let the next shift know that the coolant didn't turn on. You do not see your trainer or supervisor around. You decide to leave a note for the next shift so they are aware of the possible coolant problem. You write a sticky note and leave it on the monitor of the CNC control system.
How effective do you think this solution was? Did it address the problem?
In this scenario, you discovered several problems with the G-code that need to be addressed. When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring and avoid injury to personnel. The challenge of communicating in the workplace is learning how and when to share your ideas and concerns. If you need to tell your co-workers or supervisor that there is a problem, it is important to remember that timing and the method of communication are extremely important.
You are able to fix the coolant problem in the G-code. While you are glad that the problem is fixed, you are worried about why it happened in the first place. It is important to remember that if a problem keeps reappearing, you may not be fixing the right problem. You may only be addressing the symptoms.
You decide to talk to your trainer. Bill is glad you mentioned the problem to him. You are the third worker to mention G-code issues over the last week. You noticed the coolant problems in your G-code, John noticed a Z-axis issue in his G-code, and Sam had issues with both the Z-axis and the coolant. Chances are, there is a bigger problem and Bill will need to investigate the root cause .
Over lunch, you ask your coworkers about the G-code problem and what may be causing the error. Several people mention having similar problems but do not know the cause.
You have now talked to three coworkers who have all experienced similar coolant G-code problems. You make a list of who had the problem, when they had the problem, and what each person told you.
When you see your supervisor later that afternoon, you are ready to talk with him. You describe the problem you had with your component and the damaged bit. You then go on to tell him about talking with Bill and discovering the G-code issue. You show him your notes on your coworkers' coolant issues, and explain that you think there might be a bigger problem.
You supervisor thanks you for your initiative in identifying this problem. It sounds like there is a bigger problem and he will need to investigate the root cause. He decides to call a team huddle to discuss the issue, gather more information, and talk with the team about the importance of communication.
Root Cause Analysis
Root cause analysis ( RCA ) is a method of problem solving that identifies the underlying causes of an issue. Root cause analysis helps people answer the question of why the problem occurred in the first place. RCA uses clear cut steps in its associated tools, like the "5 Whys Analysis" and the "Cause and Effect Diagram," to identify the origin of the problem, so that you can:
- Determine what happened.
- Determine why it happened.
- Fix the problem so it won’t happen again.
RCA works under the idea that systems and events are connected. An action in one area triggers an action in another, and another, and so on. By tracing back these actions, you can discover where the problem started and how it developed into the problem you're now facing. Root cause analysis can prevent problems from recurring, reduce injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money. There are many different RCA techniques available to determine the root cause of a problem. These are just a few:
- Root Cause Analysis Tools
- 5 Whys Analysis
- Fishbone or Cause and Effect Diagram
- Pareto Analysis
How Huddles Work
Communication is a vital part of any setting where people work together. Effective communication helps employees and managers form efficient teams. It builds trusts between employees and management, and reduces unnecessary competition because each employee knows how their part fits in the larger goal.
One tool that management can use to promote communication in the workplace is the huddle . Just like football players on the field, a huddle is a short meeting where everyone is standing in a circle. A daily team huddle ensures that team members are aware of changes to the schedule, reiterated problems and safety issues, and how their work impacts one another. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.
The most important thing to remember about huddles is that they are short, lasting no more than 10 minutes, and their purpose is to communicate and identify. In essence, a huddle’s purpose is to identify priorities, communicate essential information, and discover roadblocks to productivity.
Who uses huddles? Many industries and companies use daily huddles. At first thought, most people probably think of hospitals and their daily patient update meetings, but lots of managers use daily meetings to engage their employees. Here are a few examples:
- Brian Scudamore, CEO of 1-800-Got-Junk? , uses the daily huddle as an operational tool to take the pulse of his employees and as a motivational tool. Watch a morning huddle meeting .
- Fusion OEM, an outsourced manufacturing and production company. What do employees take away from the daily huddle meeting .
- Biz-Group, a performance consulting group. Tips for a successful huddle .
One tool that can be useful in problem solving is brainstorming . Brainstorming is a creativity technique designed to generate a large number of ideas for the solution to a problem. The method was first popularized in 1953 by Alex Faickney Osborn in the book Applied Imagination . The goal is to come up with as many ideas as you can in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually. Like most problem solving techniques, brainstorming is a process.
- Define a clear objective.
- Have an agreed a time limit.
- During the brainstorming session, write down everything that comes to mind, even if the idea sounds crazy.
- If one idea leads to another, write down that idea too.
- Combine and refine ideas into categories of solutions.
- Assess and analyze each idea as a potential solution.
When used during problem solving, brainstorming can offer companies new ways of encouraging staff to think creatively and improve production. Brainstorming relies on team members' diverse experiences, adding to the richness of ideas explored. This means that you often find better solutions to the problems. Team members often welcome the opportunity to contribute ideas and can provide buy-in for the solution chosen—after all, they are more likely to be committed to an approach if they were involved in its development. What's more, because brainstorming is fun, it helps team members bond.
- Watch Peggy Morgan Collins, a marketing executive at Power Curve Communications discuss How to Stimulate Effective Brainstorming .
- Watch Kim Obbink, CEO of Filter Digital, a digital content company, and her team share their top five rules for How to Effectively Generate Ideas .
Importance of Good Communication and Problem Description
Communication is one of the most frequent activities we engage in on a day-to-day basis. At some point, we have all felt that we did not effectively communicate an idea as we would have liked. The key to effective communication is preparation. Rather than attempting to haphazardly improvise something, take a few minutes and think about what you want say and how you will say it. If necessary, write yourself a note with the key points or ideas in the order you want to discuss them. The notes can act as a reminder or guide when you talk to your supervisor.
Tips for clear communication of an issue:
- Provide a clear summary of your problem. Start at the beginning, give relevant facts, timelines, and examples.
- Avoid including your opinion or personal attacks in your explanation.
- Avoid using words like "always" or "never," which can give the impression that you are exaggerating the problem.
- If this is an ongoing problem and you have collected documentation, give it to your supervisor once you have finished describing the problem.
- Remember to listen to what's said in return; communication is a two-way process.
Not all communication is spoken. Body language is nonverbal communication that includes your posture, your hands and whether you make eye contact. These gestures can be subtle or overt, but most importantly they communicate meaning beyond what is said. When having a conversation, pay attention to how you stand. A stiff position with arms crossed over your chest may imply that you are being defensive even if your words state otherwise. Shoving your hands in your pockets when speaking could imply that you have something to hide. Be wary of using too many hand gestures because this could distract listeners from your message.
The challenge of communicating in the workplace is learning how and when to share your ideas or concerns. If you need to tell your supervisor or co-worker about something that is not going well, keep in mind that good timing and good attitude will go a long way toward helping your case.
Like all skills, effective communication needs to be practiced. Toastmasters International is perhaps the best known public speaking organization in the world. Toastmasters is open to anyone who wish to improve their speaking skills and is willing to put in the time and effort to do so. To learn more, visit Toastmasters International .
Methods of Communication
Communication of problems and issues in any workplace is important, particularly when safety is involved. It is therefore crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. As issues and problems arise, they need to be addressed in an efficient and timely manner. Effective communication is an important skill because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money.
There are many different ways to communicate: in person, by phone, via email, or written. There is no single method that fits all communication needs, each one has its time and place.
In person: In the workplace, face-to-face meetings should be utilized whenever possible. Being able to see the person you need to speak to face-to-face gives you instant feedback and helps you gauge their response through their body language. Be careful of getting sidetracked in conversation when you need to communicate a problem.
Email: Email has become the communication standard for most businesses. It can be accessed from almost anywhere and is great for things that don’t require an immediate response. Email is a great way to communicate non-urgent items to large amounts of people or just your team members. One thing to remember is that most people's inboxes are flooded with emails every day and unless they are hyper vigilant about checking everything, important items could be missed. For issues that are urgent, especially those around safety, email is not always be the best solution.
Phone: Phone calls are more personal and direct than email. They allow us to communicate in real time with another person, no matter where they are. Not only can talking prevent miscommunication, it promotes a two-way dialogue. You don’t have to worry about your words being altered or the message arriving on time. However, mobile phone use and the workplace don't always mix. In particular, using mobile phones in a manufacturing setting can lead to a variety of problems, cause distractions, and lead to serious injury.
Written: Written communication is appropriate when detailed instructions are required, when something needs to be documented, or when the person is too far away to easily speak with over the phone or in person.
There is no "right" way to communicate, but you should be aware of how and when to use the appropriate form of communication for your situation. When deciding the best way to communicate with a co-worker or manager, put yourself in their shoes, and think about how you would want to learn about the issue. Also, consider what information you would need to know to better understand the issue. Use your good judgment of the situation and be considerate of your listener's viewpoint.
Did you notice any other potential problems in the previous exercise?
- [Page 6:] Did you notice any other potential problems in the previous exercise?
Summary of Strategies
In this exercise, you were given a scenario in which there was a problem with a component you were creating on a CNC machine. You were then asked how you wanted to proceed. Depending on your path through this exercise, you might have found an easy solution and fixed it yourself, asked for help and worked with your trainer, or discovered an ongoing G-code problem that was bigger than you initially thought.
When issues and problems arise, it is important that they are addressed in an efficient and timely manner. Communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost, and save money. Although, each path in this exercise ended with a description of a problem solving tool for your toolbox, the first step is always to identify the problem and define the context in which it happened.
There are several strategies that can be used to identify the root cause of a problem. Root cause analysis (RCA) is a method of problem solving that helps people answer the question of why the problem occurred. RCA uses a specific set of steps, with associated tools like the “5 Why Analysis" or the “Cause and Effect Diagram,” to identify the origin of the problem, so that you can:
Once the underlying cause is identified and the scope of the issue defined, the next step is to explore possible strategies to fix the problem.
If you are not sure how to fix the problem, it is okay to ask for help. Problem solving is a process and a skill that is learned with practice. It is important to remember that everyone makes mistakes and that no one knows everything. Life is about learning. It is okay to ask for help when you don’t have the answer. When you collaborate to solve problems you improve workplace communication and accelerates finding solutions as similar problems arise.
One tool that can be useful for generating possible solutions is brainstorming . Brainstorming is a technique designed to generate a large number of ideas for the solution to a problem. The method was first popularized in 1953 by Alex Faickney Osborn in the book Applied Imagination. The goal is to come up with as many ideas as you can, in a fixed amount of time. Although brainstorming is best done in a group, it can be done individually.
Depending on your path through the exercise, you may have discovered that a couple of your coworkers had experienced similar problems. This should have been an indicator that there was a larger problem that needed to be addressed.
In any workplace, communication of problems and issues (especially those that involve safety) is always important. This is especially crucial in manufacturing where people are constantly working with heavy, costly, and sometimes dangerous equipment. When issues and problems arise, it is important that they be addressed in an efficient and timely manner. Effective communication is an important tool because it can prevent problems from recurring, avoid injury to personnel, reduce rework and scrap, and ultimately, reduce cost and save money.
One strategy for improving communication is the huddle . Just like football players on the field, a huddle is a short meeting with everyone standing in a circle. A daily team huddle is a great way to ensure that team members are aware of changes to the schedule, any problems or safety issues are identified and that team members are aware of how their work impacts one another. When done right, huddles create collaboration, communication, and accountability to results. Impromptu huddles can be used to gather information on a specific issue and get each team member's input.
To learn more about different problem solving strategies, choose an option below. These strategies accompany the outcomes of different decision paths in the problem solving exercise.
- View Problem Solving Strategies Select a strategy below... Root Cause Analysis How Huddles Work Brainstorming Importance of Good Problem Description Methods of Communication
Communication is one of the most frequent activities we engage in on a day-to-day basis. At some point, we have all felt that we did not effectively communicate an idea as we would have liked. The key to effective communication is preparation. Rather than attempting to haphazardly improvise something, take a few minutes and think about what you want say and how you will say it. If necessary, write yourself a note with the key points or ideas in the order you want to discuss them. The notes can act as a reminder or guide during your meeting.
- Provide a clear summary of the problem. Start at the beginning, give relevant facts, timelines, and examples.
In person: In the workplace, face-to-face meetings should be utilized whenever possible. Being able to see the person you need to speak to face-to-face gives you instant feedback and helps you gauge their response in their body language. Be careful of getting sidetracked in conversation when you need to communicate a problem.
There is no "right" way to communicate, but you should be aware of how and when to use the appropriate form of communication for the situation. When deciding the best way to communicate with a co-worker or manager, put yourself in their shoes, and think about how you would want to learn about the issue. Also, consider what information you would need to know to better understand the issue. Use your good judgment of the situation and be considerate of your listener's viewpoint.
"Never try to solve all the problems at once — make them line up for you one-by-one.” — Richard Sloma
Problem Solving: An Important Job Skill
Problem solving improves efficiency and communication on the shop floor. It increases a company's efficiency and profitability, so it's one of the top skills employers look for when hiring new employees. Recent industry surveys show that employers consider soft skills, such as problem solving, as critical to their business’s success.
The 2011 survey, "Boiling Point? The skills gap in U.S. manufacturing ," polled over a thousand manufacturing executives who reported that the number one skill deficiency among their current employees is problem solving, which makes it difficult for their companies to adapt to the changing needs of the industry.
In this video, industry professionals discuss their expectations and present tips for new employees joining the manufacturing workforce.
- [Quick Summary: Question1] What are two things you learned in this case study?
- What question(s) do you still have about the case study?
- [Quick Summary: Question2] What question(s) do you still have about the case study?
- Is there anything you would like to learn more about with respect to this case study?
- [Quick Summary: Question3] Is there anything you would like to learn more about with respect to this case study?
The Importance of Problem Solving Skills and Problem Resolution
Problem solving – the skill we all need.
In today’s world, organizations need problem solvers on their team. They need people they can rely on when the chips are down, and the levels of stress are up. People who can assess a situation with a fully lucid mind, in a calm, rational way are much sought after assets to any company. We all face challenges in our lives that require skills and experience to resolve. Our intrapersonal relationships with family members, people we deal with, our day-to-day decision making, a meeting with our doctor or bank manager may require tactful problem solving skills.
What is problem solving?
Problem solving involves first and foremost identifying the problem, secondly alternative solutions must be created. Next is the evaluation and selection of alternatives and lastly is the implementation stage where solutions are applied. It also involves diagnosing why a problem happened or happens, what factors cause it or influence it and taking action to change the pattern, therefore eliminating the problem.
To recap, the problem solving skills definition is the ability to identify and assess a certain difficult situation and having the skills to find the best or most effective solution. The person with an acute problem solving ability can hone in on the situation, and has the solution and the means to resolve the problem. Problem solving is getting to the route or cause of the problem and eliminating it. It is part of our day-to-day lives and whether the problem is big or small we all have to learn problem resolution. Finding the best way to resolve the problems so that they don’t recur and damage limitation are key to problem solving.
What are problem solving skills?
Problem-solving skills are important in all aspects of life, at work or within relationships with family and friends. Some people seem to have a natural problem solving ability. They manage to stay calm in a situation of crisis and chaos while the rest of us are unable to keep emotions out of it. If we take a closer look at the characteristics of people who are good at problem solving, we see that they have other skills that are connected to problem solving ability, some problem solving skills examples are: the ability to analyze, the ability to listen well, good communication skills, assertive decision-making ability, ability to identify and research information. Problem solving skills involve being able to make good decisions and assessing the risk of those decisions. Problem solving in the workplace is a much sought-after skill and during the selection stage many companies look for these other side-skills, easily identified during interviews, a good listener, a good communicator, researcher etc…
Problem solving in the workplace
Why is problem solving important in the workplace? The answer is simple- quick, efficient problem solving means a smoothly running organization that can stay on top of problems while focusing on their business. Problem-solving within the workplace allows us to have control over the work environment.
Problem solving examples in the workplace include risk management; being able to assess and manage risk and being aware of the cause-and-effect of what is going on in the work environment means the organization runs more smoothly. Anticipating obstacles, foreseeing problematic situations and implementing solutions are other examples. If your department has become a bit uninspired and lacking in creativity you could suggest weekly meetings to solicit new ideas and creativity. This could put the department back on its feet and get the buzz back between employees, who are given the chance to communicate their ideas with a positive outcome.
Problem solving in the workplace means improved performance where less disruption, thanks to quick-action problem solving skills, allows the business to thrive.
How can you improve problem-solving skills?
If you don’t consider yourself to be a problem solver then have a think about this; every day in our lives we have to problem solve…big and small “issues” are thrown our way daily that we have to deal with and resolve. It might just simply be coming up with a meal for dinner, getting in contact with the plumber for that broken pipe that has been leaking for two days… or making bigger decisions like changing jobs – but no-one avoids it.
Here are some ways to help you improve on your skills of problem solving :
Focus on the solution – don’t fixate on the problem. By only looking at the problem (which is causing stress) you are focusing on the negative side only, which affects how we think. That is not to say that the problem should be ignored, but focusing on solutions, trying to find the answer takes the negativity out of the situation.
Identify the problem – to help you do this, you should ask “why”. This will help you get to the cause of the problem and allow you to find a solution.
Make things simple – don’t make a stressful situation more complicated than it needs to be. Go back to basics and strip the problem bare – find the easiest and most obvious solution, which is, quite often, the simplest way.
The problem-solving skills definition includes the ability to communicate well, to think on your feet, to find the solution the easiest and quickest way possible. Lockdown Rooms offer not just a fun-filled activity, but they also help improve every single problem-solving skill. The combination of having to solve a puzzle, using communication, creativity, teamwork and listening skills will boost confidence in problem solving skills that will last long after you’ve left our escape rooms. Why not give us a call or get online to book a fun activity for your co-workers or friends?
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Brief research report article, effects of online problem-solving instruction and identification attitude toward instructional strategies on students' creativity.
- College of International Relations, Huaqiao University, Xiamen, China
Problem-solving ability is an essential part of daily life. Thus, curiosity and a thirst for knowledge should be cultivated in students to help them develop problem solving and independent thinking skills. Along with positive attitudes and an active disposition, these abilities are needed to solve problems throughout the lifespan and develop -confidence. To achieve educational objectives in the context of globalization, creative ability is necessary for generating competitive advantages. Therefore, creative thinking, critical thinking, and problem-solving ability are important basic competencies needed for future world citizens. Creativity should also be integrated into subject teaching to cultivate students' lifelong learning and a creative attitude toward life. A questionnaire was distributed to 420 students in colleges and universities in Fujian, China. After removing invalid and incomplete responses, 363 copies were found to be valid yielding a response rate of 86%. Findings indicate that the new generation requires high levels of support to develop creativity and integrate diverse subjects such as nature, humanities, and technology. A rich imagination is needed to root creativity in the new generation.
Problem solving is ubiquitous in modern life and an essential skill for overcoming the problems we encounter daily. Problems can be overcome using problem-solving principles and creative inspiration from individuals ( Hao et al., 2016 ). Thus, students' curiosity and thirst for knowledge should be cultivated to develop their problem solving and independent thinking abilities. An active approach and positive attitude to solving problems may enhance self-confidence and the ability to cope with challenges.
Education aims to cultivate healthy personalities, thinking, judgment, and creativity ( Su et al., 2014 ). Essentially, education is the learning process to expand students' potential and cultivate their ability to adapt to—and improve—their environment. Basic goals of education should include self-expression, independent thinking, active inquiry, and problem solving. The curriculum goals should be life-centered to develop individuals' potential, cultivate scientific knowledge and skills, and help students adapt to the demands of modern life ( Atmatzidou et al., 2018 ). Education aims to deliver basic knowledge, cultivate physical and mental development, inquiry, and reflection, and create healthy citizens through activities involving interaction between individuals, individuals and society, and society and nature. To achieve educational objectives, students should be guided to develop their performance and creation abilities, research and active exploration abilities, independent thinking and problem-solving abilities. In the current globalization context, creative abilities are required for building competitive advantages. Accordingly, creative thinking, critical thinking, and problem-solving abilities are key skills for future world citizens. The cultivation of creativity should also be integrated into subject instruction, so that students develop their lifelong learning and creative attitudes toward life. Many countries are eager to cultivate creative new generations and promote the development of local business and humanistic technological education. It has become a national platform for the new generations of international technological art ( Zhang and Chu, 2016 ). In particular, the traditional productivity-oriented competition model is slowly being transformed to creativity-oriented industries. Innovation capability is likely to bring competitive advantages in the Internet information age. As the field of information technology grows exponentially, innovation capability has become more important. However, if opportunities for development are missed, it can be difficult to catch up as the need for creativity is likely to grow in the foreseeable future.
In this study we focus on student creativity and how it is affected by online problem-solving instruction and identification of attitudes toward instructional strategies. Our purpose is to help the new generation develop creativity and a rich imagination to integrate the power of nature, humanities, and technology.
Literature Review and Hypothesis
Su et al. (2017) proposed that teachers who use effective instructional strategies allow students to successfully negotiate the challenges of life, as effective instructional strategies may enhance students' problem-solving ability. Deeper relationships between teachers and students also result in better learning motivation for students. Art-related activities were used to observe the factors affecting preschool children's problem-solving ability ( Calvo et al., 2018 ). These factors included the cognition of problem goals, the development of perception ability, individual experience, interaction among peers, and resource assistance provided by teachers' instructional strategies. ( LaForce et al., 2017 ) pointed out that identifying problem-related data is an essential step in the problem-solving process, i.e., the process of acquiring data, judging data, reducing data coverage, or linking relevant data ( Wu et al., 2020a ). Teachers' instructional strategies for online problem solving also affect student performance. The following hypothesis was therefore established for this study.
H1: Online problem-solving instruction has a significant positive correlation with identification attitude.
Lu et al. (2017) consider that teachers can enhance students' problem-solving ability and cultivate their problem-finding skills through instructional strategies guiding discussion of current affairs. Instructional strategies and the use of multimedia in technology education can induce students' identification attitudes and learning motivation, ultimately enhancing learning effectiveness and facilitating the development of imagination and creativity. Students with identification attitudes toward strategies could design problem-solving methods using science ( Newhouse, 2017 ). The students understood that innovation was not necessarily the novel creation of “something from nothing” but might involve modification and new development based on existing affairs ( Wu et al., 2020b ). Achilleos et al. (2019) regard attitude toward education instructional strategies as the most important factor in students' creativity learning, where teachers, social and cultural factors, and experience in learning a foreign language revealed significant correlations. Our second hypothesis was therefore presented for this study.
H2: Identification attitude shows strong positive correlations with creativity.
Hsieh et al. (2017) posit that science-related thinking, discovery, and creation can be regarded as the research component of problem solving. Creativity is characterized by keenness, fluency, flexibility, originality, and elaboration—a kind of mental intelligence to generate distinct new concepts from known experiences or knowledge to solve problems with creative methods. Creativity can also be the application of known information, based on targeted outcomes, to generate novel, unique, and valuable new concepts or a new product or technology, unexplored innovative concepts or problem-solving abilities ( Wu et al., 2021 ). Joachim et al. (2018) consider creativity as a part of problem solving, as problem-solving characteristics often involve novel thinking, strong motivation and determination to present the important status of the solution in the latent process of problem solving. However, Joachim et al.'s (2018) views on creativity and problem-solving have largely been unexplored to date. Novel performance at any level of the creative process could be considered as creation. Rietz et al. (2019) stated that life brings diverse problems and the key to addressing these lies in creativity. Only when people invest more attention in creativity can problems be solved leading to optimum solutions for life's challenges. This gives rise to our third hypothesis.
H3: Online problem-solving instruction reveals strong positive correlations with creativity
Operational definitions, online problem-solving instruction.
Referring to Chen et al. (2019) , the dimensions of online problem-solving instruction in this study were as follows.
1. Exercise example: Examples to illustrate teaching goals are provided as part of teachers' instruction. Students can learn effective problem-solving skills by observing experts' problem-solving interpretation and demonstration step-by-step.
2. Problem orientation: Problem-oriented learning refers to teachers giving carefully-designed situational problems to students, who start from a problem and proceed to problem solving and learning. After self-learning, students participate in team discussion or discussions with teachers. With constant trials, solutions are eventually proposed.
The dimensions for identification attitude toward learning are based on Tang et al. (2019) and contain the following three components.
1. Cognitive component: This refers to an individual's belief in or knowledge of specific matters. The cognition of attitude refers to evaluation of meaning from factual statements presented, i.e., an individual may form an attitude for or against a particular object. For instance, students understand that teachers have rich professional knowledge and can present materials with good organization.
2. Affective component: The affective or emotional component refers to an individual's emotions and feelings, including positive and negative feelings of respect and contempt, like and dislike, sympathy and exclusion. For example, students evaluating a teacher as a friendly person would have positive feelings about the teacher and want to develop that relationship.
3. Behavioral component: Behavior refers to an individual's response tendency to attitude objects, i.e., an individual's explicit behavioral performance when acting in relation to objects. Possible responses include approach, avoidance, or indifference. For instance, students might accept their teachers' arrangement of an activity with respect and actively ask teachers questions.
Kim et al. (2019) consider creativity includes basic cognitive abilities of divergent thinking, and that such abilities can be understood through testing tools or observation.
1. Fluency: Fluency refers to the quantity of a person's concept output, i.e., the ability to generate possible programs or solutions. A student with fluent thinking would propose several responses at the concept generation stage.
2. Flexibility: Flexibility is the ability to change thinking direction, i.e., being able to think of different methods when problems occur, to find out distinct applications or new concepts.
3. Originality: Originality refers to generation of unique and novel ideas, i.e., doing unexpected things or having the ability to see others' points of view.
4. Elaboration: Elaboration is a supplementary idea that refers to the ability to add new ideas to an original concept, i.e., the ability to increase novel concepts or build on existing ideas or basic concepts.
There are 89 colleges and universities in Fujian, China (50 colleges and 39 universities). Students in these institutions in Fujian comprised the research sample, and we distributed 420 copies of our questionnaire to them. After removing invalid and incomplete questionnaires, a total of 363 valid copies were returned, with a response rate of 86%.
This research focused on discussing online problems about teaching and teaching strategies. It used experimental design and online problem solving to do experimental research for 2 hours every week for 24 weeks (48 hours in total). To analyze data from the questionnaire, Structural Equation Modeling (SEM) was used. We followed a two-stage analysis of goodness-of-fit and model verification. Confirmatory Factor Analysis (CFA) was first executed, aiming to test complex variables in the model by deleting measured variables with negative effects on the cause-and-effect analysis. We then proceeded with path analysis with the modified model. Path analysis aims to estimate the path relationship among variables. Without testing complex variables through CFA, the path analysis might be affected by complex variables resulting in poor goodness-of-fit or an insignificant model path. Amos 18.0 was used in this study for the model fit test. The measurement result of CMIN/DF is considered good if lower than five and excellent if lower than three; Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit index (AGFI), Normed Fit Index (NFI), Incremental Fit Index (IFI), Tucker-Lewis Index (TLI), and Comparative Fit Index (CFI) are considered good if higher than 0.9; and Root Mean Square Residual (RMR), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR) are good if values are lower than 0.05.
Two factors of “exercise example” (eigenvalue = 4.638, α = 0.88) and “problem orientation” (eigenvalue = 3.751, α = 0.85) were extracted from the scale of instructional strategies for online problem solving. The cumulative covariance accounted for was 72.683%. Three factors were extracted from the identification attitude scale: “cognitive component” (eigenvalue = 2.463, α = 0.81), “affective component” (eigenvalue = 1.754, α = 0.83), and “behavioral component” (eigenvalue = 1.491, α = 0.84). The cumulative covariance reached 73.942%. Four factors were extracted from the creativity scale: “fluency” (eigenvalue = 2.461, α = 0.84), “flexibility” (eigenvalue = 2.055, α = 0.82), “originality” (eigenvalue = 1.976, α = 0.87), and “elaboration” (eigenvalue = 1.689, α = 0.86). The cumulative covariance accounted for was 79.317%.
Empirical Analysis of SEM
CFA results indicated the convergent and discriminant validity of the model were first observed, with convergent validity describing the reliability of individually observed variables, construct reliability (CR), and average variances extracted (AVE). Values of more than 0.5 indicate good reliability of individually observed variables. The factor loadings of the observed variables in the empirical analysis model were higher than the suggested value. CR should exceed 0.6, although some researchers suggest that 0.5 or above is acceptable. The model calibration results reveal CR was higher than 0.6, and AVE higher than 0.5, thus conforming to the suggested values.
Regarding the calibration results of structural equations, χ 2 / df , RMSEA, GFI, AGFI, RMR, and NFI were also calculated. For χ 2 / df a standard ≦5 is suggested and χ 2 / df = 2.422 ≦ 5 in this study. The standard for RMSEA is ≦0.08; reported here as 0.044 ≦ 0.08. GFI has a suggested standard of ≧0.9 and here it is reported as 0.951 ≧ 0.9. AGFI's suggested standard ≧0.9; it shows AGFI = 0.927 ≧ 0.9 in this study. RMR has a suggested standard of ≦0.05, and here it was reported as 0.023 ≦ 0.05. The NFI standard is ≧0.9; here it presents NFI = 0.937 ≧ 0.9 in this study. The overall model fit is good. The parameter calibration of the structural equation is shown in Table 1 and Figure 1 . The research results reveal instructional strategies for online problem solving → identification attitude: 0.346 *** , that is, H1 was supported. Identification attitude → creativity: 0.375 *** , that is, H2 was supported, and instructional strategies for online problem solving → creativity: 0.425 *** , that is, H3 was supported.
Table 1 . Structural equation modeling result.
Figure 1 . FigureModel path diagram.
The results show that online instructional strategies for online problem solving can enhance students' creativity. Apparently, an expository teaching style is no longer sufficient to cope with challenges encountered. Rather, teachers need to be willing to constantly learn and change their teaching behavior to cope with the rapid development of new technology and enhance teaching efficiency. When conveying new knowledge to beginners, the provision of exercise examples may help students establish new schema to benefit the application to similar situations. When lacking relevant schema, beginners may try to solve problems with trial and error. In this case, exercise examples with experts demonstrating problem-solving steps could benefit students' learning performance in the new field. Problems studied in real life may facilitate students' creativity, drawing on their existing knowledge as they use available resources and unconsciously apply existing knowledge to enhance creative ability. The solutions to problems are unpredictable but require the ability to cope with interaction between people in a given culture or society in different situations. As a result, teachers should make decisions with the consideration of situational changes in the teaching site, i.e., students' ability, performance and teaching schedule, rather than generalizing across all situations. We do not suggest limiting creative thinking or defining set times for enhancing students' creative thinking. Instead, factors that influence creative efficiency, creative value, and curriculum schedules should be taken into account. As teachers plan their teaching activities, they should pay particular attention to students' academic performance and the vicarious experience of teachers or peers. Uysal (2014) believed people can develop their mental ability through learning even without any creative invention. When we face any new concept, it is better to keep an open mind. That way we will realize there is still a lot to be created ( Fernández et al., 2018 ). Labusch et al. (2019) said the development of creativity is not only creating positive thoughts but also turning these advantages into something more refined and broader. Teachers need to provide learning opportunities that students can apply in their daily lives leading to a re-evaluation of their identification attitudes toward instructional strategies. In this case, enhancing students' self-efficacy may assist them in overcoming learning challenges and cultivating a more positive learning attitude.
The research results demonstrate that online problem solving supports students to examine their ideas, chase after knowledge and continually improve their learning. They can freely develop their imaginations and make choices without being limited to find tools suitable for self-performance. They can concentrate on details, retain memories, and calmly think of more elaborate problem-solving approaches. Students draw on plans and organization to make significant progress in their thinking depth, novelty, flexibility, unique style, and diversity of function. To cultivate students' habits of brainstorming and thinking, they must become familiar with the general use of contextual information, and flexibility to change approaches and seek answers. Training flexible thinking is essential so that students can cope with problems with ease, propose various options and generate solutions. Lumsdaine and Lumsdaine (1995) let students learn from each other and modify their own thought. This transition could help them to achieve their potential. Solitary and monotonous learning material can no longer attract students' attention. Teachers need to provide a wider variety of materials and free choices without limit. They could also find more suitable tools for teaching. Therefore, Treffinger and Isaksen (1992) no longer provide model answers. They want students to explore and develop without any restriction. This could also amplify their personal experience and bring more options into it. It enhances student's uniqueness, and this needs overall growth, subjectively and objectively. People should never venerate one over the other. We should also learn to make good use of the conditions and things we already have. The same thing could have an entirely different outcome depending on how we use it ( Aşik and Erktin, 2019 ). Consequently, problem-solving instruction could assist in the cultivation of creativity in students' practice ability or cultivation of independent thinking and problem-solving ability. Teachers should attempt to create beneficial educational environments, cultivating students' learning interests, and enhancing their mental development. With accumulated experience, students can then be encouraged to develop more flexible skills, sensitive perception, and active thinking along with the ability to appropriately express these experiences. This would provide comprehensive preparation for enhancing students' creative thinking ability. Instructional strategies for online problem solving heavily emphasize cooperative discussion, brainstorming, and presentation. Tasks focusing on students' favorite novels and other relevant interests are valuable for sustaining long-term attention. Success in learning does not simply rely on rich knowledge and skillful techniques; affective attitudes also play an important part. Such characteristics may encourage students to positively and actively face problems and logically enhance their learning attitudes step-by-step.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The studies involving human participants were reviewed and approved by the Ethical Committee of the Huaqiao University. The patients/participants provided their written informed consent to participate in this study.
Y-PW performed the initial analyses and approved the submitted version of the manuscript.
Conflict of Interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The authors thank the reviewers for their valuable comments.
Achilleos, A. P., Mettouris, C., Yeratziotis, A., Papadopoulos, G. A., Pllana, S., Huber, F., et al. (2019). SciChallenge: a social media aware platform for contest-based STEM education and motivation of young students. IEEE Trans. Learn. Technol . 12, 98–111. doi: 10.1109/TLT.2018.2810879
CrossRef Full Text | Google Scholar
Aşik, G., and Erktin, E. (2019). Metacognitive experiences: mediating the relationship between metacognitive knowledge and problem solving. Egitim ve Bilim 44, 85–103. doi: 10.15390/EB.2019.7199
Atmatzidou, S., Demetriadis, S., and Nika, P. (2018). How does the degree of guidance support students' metacognitive and problem solving skills in educational robotics? J. Sci. Educ. Technol. 27, 70–85. doi: 10.1007/s10956-017-9709-x
Calvo, I., Cabanes, I., Quesada, J., and Barambones, O. (2018). A multidisciplinary PBL approach for teaching industrial informatics and robotics in engineering. IEEE Trans. Educ . 61, 21–28. doi: 10.1109/TE.2017.2721907
Chen, S.-Y., Lai, C.-F., Lai, Y.-H., and Su Y, S. (2019). Effect of project-based learning on development of students' creative thinking. Int. J. Electr. Eng. Educ . doi: 10.1177/0020720919846808
Fernández, J., Zúñiga, M. E., Rosas, M. V., and Guerrero, R. A. (2018). Experiences in learning problem-solving through computational thinking. J. Comput. Sci. Technol. 18, 136–142. doi: 10.24215/16666038.18.e15
Hao, J., Liu, L., von Davier, A., Kyllonen, P., and Kitchen, C. (2016). “Collaborative problem solving skills vs. collaboration outcomes: findings from statistical analysis and data mining,” in Proceedings of the 9th International Conference on Educational Data Mining (Raleigh, NC), 382–387.
Hsieh, J. S. C., Huang, Y.-M., and Wu, W.-C. V. (2017). Technological acceptance of LINE in flipped EFL oral training. Comput. Hum. Behav . 70, 178–190. doi: 10.1016/j.chb.2016.12.066
Joachim, V., Spieth, P., and Heidenreich, S. (2018). Active innovation resistance: an empirical study on functional and psychological barriers to innovation adoption in different contexts. Ind. Mark. Manag. 71, 95–107. doi: 10.1016/j.indmarman.2017.12.011
Kim, J., Jordan, S. S., Franklin, C., and Froerer, A. (2019). Is solution-focused brief therapy evidence-based: an update 10 years later. Fam. Soc. 100, 127–138. doi: 10.1177/1044389419841688
Labusch, A., Eickelmann, B., and Vennemann, M. (2019). “Computational thinking processes and their congruence with problem-solving and information processing,” in Proceedings of the Computational Thinking Education , 65–78. doi: 10.1007/978-981-13-6528-7_5
LaForce, M., Noble, E., and Blackwell, C. K. (2017). Problem-based learning (PBL) and student interest in STEM careers: the roles of motivation and ability beliefs. Educ. Sci. 7:92. doi: 10.3390/educsci7040092
Lu, X., Wang, D., and Yu, D. (2017). Effect of solution-focused brief therapy-based on exercise prescription intervention on adolescent mental health. Rev. Argentina de Clin. Psicol . 26, 347–354. doi: 10.24205/03276716.2017.1035
Lumsdaine, E., and Lumsdaine, M. (1995). Creative Problem Solving-Thinking Skills for Changing World . New York, NY: McGRAW-Hill. doi: 10.1109/45.464655
Newhouse, C. (2017). STEM the boredom: engage students in the Australian curriculum using ICT with problem-based learning and assessment. J. Sci. Educ. Technol. 26, 44–57. doi: 10.1007/s10956-016-9650-4
Rietz, T., Benke, I., and Maedche, A. (2019). “The impact of anthropomorphic and functional chatbot design features in enterprise collaboration systems on user acceptance,” in Proceedings of the 14th International Conference Wirtschaftsinformatik (Siegen), 1642-1656.
Su, Y.-S., Ding, T.-J., and Lai, C.-F. (2017). Analysis of students' engagement and learning performance in a social community supported computer programming course. Eurasia J. Math. Sci. Technol. Educ . 13, 6189–6201. doi: 10.12973/eurasia.2017.01058a
Su, Y. S., Yang, J. H., Hwang, W. Y., Huang, S. J., and Tern, M. Y. (2014). Investigating the role of computer-supported annotation in problem solving based teaching: an empirical study of a scratch programming pedagogy. Br. J. Educ. Technol. 45, 647–665. doi: 10.1111/bjet.12058
Tang, K. Y., Hsiao, C. H., and Su, Y. S. (2019). Networking for educational innovations: a bibliometric survey of international publication patterns. Sustainability 11:4608. doi: 10.3390/su11174608
Treffinger, D. J., and Isaksen, S. G. (1992). Creative Problem Solving: An Introduction . Montgomery: Center of Creative Learning, Inc.
Uysal, M. P. (2014). Improving first computer programming experiences: the case of adapting a web-supported and well-structured problem-solving method to a traditional course. Contemp. Educ. Technol . 5, 198–217. doi: 10.30935/cedtech/6125
Wu, T. J., Yuan, K. S., and Yen, D. C. (2021). Leader-member exchange, turnover intention and presenteeism–the moderated mediating effect of perceived organizational support. Curr. Psychol . doi: 10.1007/s12144-021-01825-1
Wu, T. J., Gao, J. Y., Wang, L. Y., and Yuan, K. S. (2020a). Exploring links between polychronicity and job performance from the person–environment fit perspective-the mediating role of well-being. Int. J. Environ. Res. Public Health 17, 3711–3722. doi: 10.3390/ijerph17103711
PubMed Abstract | CrossRef Full Text | Google Scholar
Wu, T. J., Xu, T., Li, L. Q., and Yuan, K. S. (2020b). “Touching with heart, reasoning by truth”! The impact of Brand cues on mini-film advertising effect. Int. J. Advert . 39, 1322–1350. doi: 10.1080/02650487.2020.1755184
Zhang, Y., and Chu, S. K. W. (2016). New ideas on the design of the web-based learning system oriented to problem solving from the perspective of question chain and learning community. Int. Rev. Res. Open Dis. 17, 176–189. doi: 10.19173/irrodl.v17i3.2115
Keywords: online problem, instructional strategies, identification attitude, affective component, creativity
Citation: Wang Y-P (2021) Effects of Online Problem-Solving Instruction and Identification Attitude Toward Instructional Strategies on Students' Creativity. Front. Psychol. 12:771128. doi: 10.3389/fpsyg.2021.771128
Received: 05 September 2021; Accepted: 27 September 2021; Published: 14 October 2021.
Copyright © 2021 Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yi-Ping Wang, email@example.com
This article is part of the Research Topic
Digital Transformation of Education in the Covid-19 Process and its Psychological Effects on Children
Importance Of Problem-Solving Skills In The Workplace
November 10, 2022
Oxford Languages defines problem-solving as the process of finding solutions that help resolve complicated issues. Problems needing solutions vary from fundamental personal issues of "how do I turn on this laptop?" to more complex topics in academics and business.
What are problem-solving skills?
Problem-solving skills help us solve issues quickly and effectively and hold much importance in our personal and professional lives.
As humans, we all deal with problems. Even if we do not know how to fix the problem, we can process it and understand why it happened in the first place.
Then, we can apply logic to find some solutions to resolve it—no wonder most employers seek problem-solving skills in candidates. Solving problems is beneficial in almost every job role and can help support competency management .
What do problem-solving skills in the workplace look like?
At the core of business development, problem-solving skills enable employees to leverage the available resources to arrive at a solution productively that syncs with the company's integrity. Some examples of problem-solving in the workplace include:
- Handling and resolving a conflict with a coworker
- Troubleshooting and eliminating technical issues in a work laptop
- Solving any problems related to accounting, taxation, and customer billing
- Taking the initiative when a team member missed or overlooked something important
- Extracting a new piece of data that can guide a company's strategy in a specific area
In a nutshell, problem-solving is a part of everyone's work - whether they are a manager with years of experience or starting their first job. It may test your critical-thinking skills or involve the use of mathematical operations.
Why are problem-solving skills important in the workplace?
When prospective employers talk about problem-solving, their goal is to measure how candidates support the decision-making process in the company's everyday functioning by applying this skill. Here are four reasons why problem-solving is an important skill to have in the workplace:
1. Strategy prioritization, planning, and execution
Efficient problem-solvers can carefully assess customer requirements and put together a plan that helps them provide a brilliant service to their intended audience. Their forte lies in streamlining processes by removing bottlenecks.
2. Out-of-the-box thinking
Problem-solving and creative thinking go hand-in-hand. It is not a matter of putting a bandaid over an issue but finding a way to fix it dynamically and creatively. This attitude helps the business stay ahead of the curve and improves the workforce's capability over time.
3. Better time management
When a problem arises, it needs to be fixed at the earliest. Employees with excellent problem-solving skills are laser-focused on what is vital to the business and can roll with the punches and tight deadlines to deliver when it matters.
4. Risk management
Planning effectively is an essential problem-solving skill to have. Problem-solvers can react quickly to short-term situations without losing sight of the future. Their positive attitude towards learning agility helps them anticipate problems that may arise in the future based on past experiences, industry trends and patterns, and current events.
What are the four stages of problem-solving in the workplace?
Understanding the critical components involved in problem-solving will help you better demonstrate your expertise to your managers. Businesses rely on people who can assess different types of situations and calmly identify solutions. Strong problem-solvers are a valuable addition to any team. Here are the four stages of problem-solving that you must know about:
1. Understand the problem
First, analyze the situation carefully to learn more about the problem. Many employees jump to providing solutions before determining the cause of the problem. Try anticipating the behavior and response of the people affected by it.
Based on your preliminary observation, undertake the following activities to pinpoint the problem more accurately:
- Separate facts from opinions.
- Analyze company policies and procedures.
- Determine the process where the problem exists.
- Discuss with team members involved to gather more information.
When defining a problem, please stay focused on it rather than define it in terms of a solution at this stage. For instance, if the sales numbers need to be consistent in the next quarter, simply say the sales numbers are inconsistent.
A quick way to verify whether you understand what you are dealing with is to explain it to a fellow team member. This ties into your communication skills. If you can talk about the issue clearly with others, it means you understand the case in your hands.
Depending on the complexity of the problem, you may want to use flowcharts or cause-and-effect graphs to define the problem and its root causes.
2. List all possible solutions
Workplace solutions are either strategic or tactical. A strategic solution is a long-term fix for an issue. This requires you to take a series of steps to process the entire architecture of the problem.
On the other hand, a tactical solution is more of a short-term fix. It could be as simple as reusing a piece of code from your last development project to eliminate the irritating error message in your new one.
Brainstorm all possible ways to solve the existing problem. Problem-solving delivers better results with cognitive diversity . Therefore, make sure you get the team members affected by it and those who may have more experience with the type of challenge you are facing to brainstorm potential solutions. Conduct discussions face-to-face or virtually and use surveys.
Spot and remove every aspect that slows down your problem-solving. Ensure the ideas and suggestions put forward to align with the relevant goals and objectives. Create a list of 5-8 long-term and short-term solutions.
3. Evaluate the solutions
Assess the pros and cons of each solution and identify the resources required for their implementation, including personnel, time allocation, budget constraints, and data requirements. Ask yourself the following questions:
- Is the solution practical and easy to implement?
- Is it acceptable to everyone involved?
- Does it solve the problem smoothly without creating another problem?
- Does it fit within the company's procedures and policies?
The ability to promptly evaluate solutions ties into your management skills. Train yourself to identify as many parameters as possible, such as duration, efficiency, cost-effectiveness, and practicality, so you can come up with the most effective solution in a shorter period.
4. Implement and monitor the progress of the chosen solution
Create an action plan to implement the solution. Define its objectives and divide them into measurable targets for monitoring the implementation. Set timelines for implementation. Communicate the plan to everyone involved.
After that, continuously monitor the progress to ensure your solution works. Gather feedback from others and data to check whether the solution helps resolve the problem. You may need to adjust the resolution if anything unexpected arises, so please be prepared for that.
What are the critical problem-solving skills in the workplace?
Problem-solving skills enable you to seek and engage candidates who are cognitively equipped to handle anything their jobs throw at them. Problem solvers can observe, judge, and act quickly when the situation demands it- without negatively impacting the business. Here are the top problem-solving skills in the workplace:
1. Decision-making prowess
Decision-making skills are an essential part of problem-solving. That is because you can only solve it if you wholly understand the problem and decide to do something about it. Decision-making skills help professionals quickly choose between two or more alternatives after evaluating the pros and cons of each.
Successfully communicating the problem and recommending solutions for it verbally and in writing is an art in itself. Proper communication ensures solutions are carried out effectively, and everyone involved in the conflict is on the same page.
Open-mindedness is essentially the willingness to look at things from a different perspective and consider new ideas. When you have a problem in front of you, review its various aspects to come up with the best solution possible. Being curious and aware helps one be a better problem-solver.
4. Analytics skills
Nearly all problem-solving cases require some form of analysis - forecasting, critical thinking, or troubleshooting. Analytical skills empower you to understand the problem better and develop practical solutions based on facts and evidence.
Collaboration is the key to ensuring communication lines are always open, problems are addressed cooperatively, and the team's objectives precede personal goals.
Team dynamics are critical to problem-solving as it helps you work well with others towards a common goal. Necessary collaboration skills include conflict resolution, emotional intelligence, empathy, sensitivity, and respect.
Over to you
In hiring, pre-employment aptitude tests assess qualities critical to most jobs, such as critical thinking, abstract reasoning, attention to detail, and so on. Problem-solving, specifically, is also a significant predictor of job performance .
However, this skill is as varied as the issues it is applied to. The best problem solvers use the same basic approach to identify and solve problems and incorporate all the skills and steps discussed in the blog post for successful results.
Adaface's aptitude tests help evaluate a candidate's ability to understand instruction, analyze the information at hand, and respond to complicated situations or problems.
The questions are designed to fetch insights into the candidates' problem-solving and coachability. Check them out!
- Problem-solving test
- Logical reasoning test
- Critical thinking test
Asavari is an EiR at Adaface. She has made it her mission to help recruiters deploy candidate-friendly skill tests instead of trick-question based tests. When taking a break, she obsesses over art.
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What is problem-solving? And why is it important in the workplace?
September 28, 2023 by MindManager Blog
If there’s one thing you can count on as a business professional, it’s that you’ll never run short of new problems to solve. Thankfully, whether it includes handling difficult or unexpected situations in the workplace, or resolving complex organizational challenges, we all have the capacity to develop our business problem-solving skills.
The best way to get better at tackling problems productively is to start at the beginning. After all, the better you understand what problem-solving is – and the significant role it plays in every organization – the easier you’ll find it to improve on problem-solving skills in the workplace.
Let’s dive in!
What is problem-solving?
Problem-solving refers to the act of find solutions to difficult or complex issues.
A good problem-solving definition might be finding solutions to difficult or complex issues . In practice, however, solving problems in the workplace is a little more immersive than that.
In the workplace, problem-solving includes a variety of tools, resources, and techniques to:
- Identify what’s not working.
- Figure out why it’s broken.
- Determine the best course of action to fix it.
Whether you know them as obstacles, glitches, or setbacks, problems are a part of our everyday lives. The good news is that our brains excel at reasoning out intricate scenarios and making calculations in situations we’ve never experienced before. That means every one of us is hard-wired to be an adept problem-solver.
The trick is to learn how to take that innate ability and apply it in a deliberate and practiced way.
However, one thing is certain: successfully resolving business and workplace issues is essential.
Not only does effective problem-solving create value that encourages growth, it goes hand-in-hand with impactful decision making.
What are the benefits of problem-solving in business?
Practically speaking, problem-solving provides a golden opportunity to improve your processes, products, and systems – especially when you work through those challenges with others.
Learning to face difficulties calmly, and deal with them intentionally, can also:
- Ramp up your confidence.
- Increase your resilience.
- Help you develop valuable critical thinking skills.
Applying problem-solving skills in the face of an obstacle that seems insurmountable trains you to shift your perspective and look at potential hurdles in a different way.
It also gets you used to examining multiple options for dealing with a problem, which can help you feel more confident in the direction you take.
Solving problems as a team
Business problem solving as a team offers an even wider range of benefits since active collaboration tends to make good things happen at both the individual and group level.
- Team-based problem-solving is akin to having a built-in sounding board when you explore new approaches and ideas.
- As each team member’s critical thinking skills evolve, they bring fresh insights to the collective problem-solving process, bearing out the old adage that many heads are better than one.
- Solving problems as a team also reduces the feeling of personal risk and exposure that’s common when one person is tasked with solving a puzzle. When that same problem is shared, the sense of risk gets dispersed, and individual team members are less likely to feel singled out.
Not only is there less chance of arriving at an unreasonable or biased solution when you problem-solve as a group, team members assigned to carry that solution out will feel more invested in its success.
Examples of problem solving skills in the workplace
Improving on your problem-solving skills helps you make the most of your brain’s natural capacity to analyze and reason things out.
There are dozens of problem-solving skills that play out in the average workplace – all of which can contribute to your ability to correct oversights, resolve conflict , and work around unexpected obstructions.
Here are a few common examples of problem-solving skills in the workplace, and tips on how to improve them.
1. Data gathering
Figuring out the cause of a problem hinges on collecting relevant data. Consulting efficiently with colleagues, conducting online research, and brainstorming with your team are all valuable data gathering skills.
2. Active listening
As opposed to listening in a purely supportive or empathetic way, active listening involves concentrating fully on what the other person is saying so you can understand the content, respond accordingly, and remember what was said later.
The ability to analyze and troubleshoot a situation with the help of any data and human input you’ve gathered is essential for drilling down into the core of a problem, and scrutinizing potential solutions.
Brainstorming has become synonymous with creative thinking, innovative idea generation, and problem-solving. The more productive your brainstorming sessions, the more likely you and your group are to put together a list of quality, workable solutions.
It’s interesting to note that effective decision making is both a contributor to, and a by-product of, effective problem-solving.
For example, honing your analytical abilities and other problem-solving skills will inevitably help you make better decisions. The more efficient your decision-making process becomes, meanwhile, the better you’ll get at uncovering and acting on the most promising solution to any dilemma.
A simple problem-solving scenario
It’s clear that we can all benefit from getting more comfortable with problem-solving in the workplace.
Examples of situations where your problem-solving skills will come in handy aren’t difficult to find, and might include:
- Fixing a technical issue for your customer.
- Improving your student’s test performance.
- Reducing the theft of your in-store merchandise.
- Bumping up your marketing reach.
But, here’s the interesting thing. While it’s evident in each of these situations that there’s a problem to be solved, the exact nature of that problem isn’t so obvious.
In the student’s case, for example, you’d need additional input to help you figure out why they’re performing poorly. Only then would you be able to take steps to find the best-fit solution and achieve the desired learning outcome.
Here’s a simple scenario to help demonstrate that idea:
Bringing new customers onboard in a timely manner is an important part of your client relations strategy. Since hiring Alex a few weeks ago, however, your onboarding process has been taking longer than it should and team members are beginning to complain.
While you can see that the problem in this scenario is the fact that your team isn’t meeting their client onboarding goals, the key is to get clear on exactly what’s causing the hold-up.
You could jump to the conclusion that Alex has time management issues and that it’s time to start looking for a replacement. But, since one of the most common mistakes in business problem-solving is attempting to seize on a solution right away, that might cause you to waste time and resources on a remedy that ultimately proves unnecessary, or that doesn’t provide a viable fix.
Instead, it’s time to put your problem-solving skills to work.
Using data gathering and troubleshooting to pinpoint and clarify the bottleneck in your onboarding process – and active listening to interpret the situation from Alex’s perspective – you soon determine that the real cause of the problem is not what you thought.
In truth, an administrative oversight during the hiring process (yet another problem to be solved!) left Alex unaware of, and without access to, the business process map that’s so vital to efficiently onboarding new customers. Once you provide the necessary resources, it doesn’t take Alex long to get up to speed – and your client onboarding process to revert back to the well-oiled machine that it was.
Even with a team of eager problem-solvers by your side, the truth is that it’s often necessary to have the right problem-solving tools in place to achieve your desired results. That’s where versatile mind mapping software can help.
Not only does MindManager® provide a visual framework that fully supports the problem-solving process, it improves comprehension, inspires more creative solutions, and boosts your ability to make the best possible decisions.
Ready to take the next step?
MindManager helps boost collaboration and productivity among remote and hybrid teams to achieve better results, faster.
Why choose MindManager?
MindManager® helps individuals, teams, and enterprises bring greater clarity and structure to plans, projects, and processes. It provides visual productivity tools and mind mapping software to help take you and your organization to where you want to be.
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The importance of problem solving as a skill.
Problem Solving as an ability is a life skill wanted by many, as it is essential to our day-to-day lives. Whether we are at home, or school or work, we are thrown curve balls by life almost every single step of the way. And how do we resolve those? You guessed it right – Problem Solving.
In this fast-changing era, employers often recognize everyday problem solving as integral to the success of their organizations. Developing a problem-solving element for employees can be used for practical and creative solutions, and to show independence and initiative to employers. This skill can empower you in your personal and professional life.
Solving problems can sometimes be an unconscious effort, as we solve problems every day without really thinking about them. It just happens. Nevertheless, in order to be effective at problem solving, some other key skills have to be kept in mind.
- Emotional Intelligence – It is the capacity to be aware of, or to recognize the emotions of oneself, or others and to handle interpersonal relationships with empathy. A problem or its solution usually creates an impact on you or others. It is worth considering, as it will help guide you to an appropriate solution. It is imperative to have strong observational skills.
- Creativity – There are two ways to solve problems, either intuitively or systematically. Intuition is used when no new knowledge is needed which results in a quick decision using common sense or experience. A systematic or logical approach is used if the problems are more complex along with some creative thinking. Using critical thinking and attention to detail helps in assessing the results.
- Team Work – You may not have all the answers to your problems. That is when the input of other people comes in handy. Whether at home or school, ‘team work’ is an important aspect to problem solving. Someone may be more of an innovative thinker comparatively, so you could play well with the team in order to demonstrate persistence to explore potential solutions. Both communication and negotiation is important at this point.
- Researching Skills – Solving problems often requires a little bit of research. It can be a simple Google Search or an in-depth research project.
- Risk Management – A certain amount of risk is always involved when solving problems. The key is to weigh those risks against the problem like a pros and cons list. Then, take the leap of faith.
- Decision Making – Problem Solving and Decision Making go hand in hand as a skill set. In order to solve a problem, a decision has to be made. Sometimes, having enough information about the problem is what can influence a decision. Demonstrating lateral thinking and analytical abilities is important rather than accepting issues at face value. It will help you to assess what is exactly going on.
Problem solving is important in every trade. There is no aspect of life that is immune to the regular onslaught of troubles. Improving your problem solving skills will give you a divergent edge that will certainly make you stand out from the crowd.
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