problem solving learning style

Problem based learning: a teacher's guide

December 10, 2021

Find out how teachers use problem-based learning models to improve engagement and drive attainment.

Main, P (2021, December 10). Problem based learning: a teacher's guide. Retrieved from https://www.structural-learning.com/post/problem-based-learning-a-teachers-guide

What is problem-based learning?

Problem-based learning (PBL) is a style of teaching that encourages students to become the drivers of their learning process . Problem-based learning involves complex learning issues from real-world problems and makes them the classroom's topic of discussion ; encouraging students to understand concepts through problem-solving skills rather than simply learning facts. When schools find time in the curriculum for this style of teaching it offers students an authentic vehicle for the integration of knowledge .

Embracing this pedagogical approach enables schools to balance subject knowledge acquisition with a skills agenda . Often used in medical education, this approach has equal significance in mainstream education where pupils can apply their knowledge to real-life problems. 

PBL is not only helpful in learning course content , but it can also promote the development of problem-solving abilities , critical thinking skills , and communication skills while providing opportunities to work in groups , find and analyse research materials , and take part in life-long learning .

PBL is a student-centred teaching method in which students understand a topic by working in groups. They work out an open-ended problem , which drives the motivation to learn. These sorts of theories of teaching do require schools to invest time and resources into supporting self-directed learning. Not all curriculum knowledge is best acquired through this process, rote learning still has its place in certain situations. In this article, we will look at how we can equip our students to take more ownership of the learning process and utilise more sophisticated ways for the integration of knowledge .

Philosophical Underpinnings of PBL

Problem-Based Learning (PBL), with its roots in the philosophies of John Dewey, Maria Montessori, and Jerome Bruner, aligns closely with the social constructionist view of learning. This approach positions learners as active participants in the construction of knowledge, contrasting with traditional models of instruction where learners are seen as passive recipients of information.

Dewey, a seminal figure in progressive education, advocated for active learning and real-world problem-solving, asserting that learning is grounded in experience and interaction. In PBL, learners tackle complex, real-world problems, which mirrors Dewey's belief in the interconnectedness of education and practical life.

Montessori also endorsed learner-centric, self-directed learning, emphasizing the child's potential to construct their own learning experiences. This parallels with PBL’s emphasis on self-directed learning, where students take ownership of their learning process.

Jerome Bruner’s theories underscored the idea of learning as an active, social process. His concept of a 'spiral curriculum' – where learning is revisited in increasing complexity – can be seen reflected in the iterative problem-solving process in PBL.

Webb’s Depth of Knowledge (DOK) framework aligns with PBL as it encourages higher-order cognitive skills. The complex tasks in PBL often demand analytical and evaluative skills (Webb's DOK levels 3 and 4) as students engage with the problem, devise a solution, and reflect on their work.

The effectiveness of PBL is supported by psychological theories like the information processing theory, which highlights the role of active engagement in enhancing memory and recall. A study by Strobel and Van Barneveld (2009) found that PBL students show improved retention of knowledge, possibly due to the deep cognitive processing involved.

As cognitive scientist Daniel Willingham aptly puts it, "Memory is the residue of thought." PBL encourages learners to think critically and deeply, enhancing both learning and retention.

Here's a quick overview:

• John Dewey : Emphasized learning through experience and the importance of problem-solving.
• Maria Montessori : Advocated for child-centered, self-directed learning.
• Jerome Bruner : Underlined learning as a social process and proposed the spiral curriculum.
• Webb’s DOK : Supports PBL's encouragement of higher-order thinking skills.
• Information Processing Theory : Reinforces the notion that active engagement in PBL enhances memory and recall.

This deep-rooted philosophical and psychological framework strengthens the validity of the problem-based learning approach, confirming its beneficial role in promoting valuable cognitive skills and fostering positive student learning outcomes.

What are the characteristics of problem-based learning?

Adding a little creativity can change a topic into a problem-based learning activity. The following are some of the characteristics of a good PBL model:

• The problem encourages students to search for a deeper understanding of content knowledge;
• Students are responsible for their learning. PBL has a student-centred learning approach . Students' motivation increases when responsibility for the process and solution to the problem rests with the learner;
• The problem motivates pupils to gain desirable learning skills and to defend well-informed decisions ;
• The problem connects the content learning goals with the previous knowledge. PBL allows students to access, integrate and study information from multiple disciplines that might relate to understanding and resolving a specific problem—just as persons in the real world recollect and use the application of knowledge that they have gained from diverse sources in their life.
• In a multistage project, the first stage of the problem must be engaging and open-ended to make students interested in the problem. In the real world, problems are poorly-structured. Research suggests that well-structured problems make students less invested and less motivated in the development of the solution. The problem simulations used in problem-based contextual learning are less structured to enable students to make a free inquiry.

• In a group project, the problem must have some level of complexity that motivates students towards knowledge acquisition and to work together for finding the solution. PBL involves collaboration between learners. In professional life, most people will find themselves in employment where they would work productively and share information with others. PBL leads to the development of such essential skills . In a PBL session, the teacher would ask questions to make sure that knowledge has been shared between pupils;
• At the end of each problem or PBL, self and peer assessments are performed. The main purpose of assessments is to sharpen a variety of metacognitive processing skills and to reinforce self-reflective learning.
• Student assessments would evaluate student progress towards the objectives of problem-based learning. The learning goals of PBL are both process-based and knowledge-based. Students must be assessed on both these dimensions to ensure that they are prospering as intended from the PBL approach. Students must be able to identify and articulate what they understood and what they learned.

Why is Problem-based learning a significant skill?

Using Problem-Based Learning across a school promotes critical competence, inquiry , and knowledge application in social, behavioural and biological sciences. Practice-based learning holds a strong track record of successful learning outcomes in higher education settings such as graduates of Medical Schools.

Educational models using PBL can improve learning outcomes by teaching students how to implement theory into practice and build problem-solving skills. For example, within the field of health sciences education, PBL makes the learning process for nurses and medical students self-centred and promotes their teamwork and leadership skills. Within primary and secondary education settings, this model of teaching, with the right sort of collaborative tools , can advance the wider skills development valued in society.

At Structural Learning, we have been developing a self-assessment tool designed to monitor the progress of children. Utilising these types of teaching theories curriculum wide can help a school develop the learning behaviours our students will need in the workplace.

Curriculum wide collaborative tools include Writers Block and the Universal Thinking Framework . Along with graphic organisers, these tools enable children to collaborate and entertain different perspectives that they might not otherwise see. Putting learning in action by using the block building methodology enables children to reach their learning goals by experimenting and iterating. 

How is problem-based learning different from inquiry-based learning?

The major difference between inquiry-based learning and PBL relates to the role of the teacher . In the case of inquiry-based learning, the teacher is both a provider of classroom knowledge and a facilitator of student learning (expecting/encouraging higher-order thinking). On the other hand, PBL is a deep learning approach, in which the teacher is the supporter of the learning process and expects students to have clear thinking, but the teacher is not the provider of classroom knowledge about the problem—the responsibility of providing information belongs to the learners themselves.

As well as being used systematically in medical education, this approach has significant implications for integrating learning skills into mainstream classrooms .

Using a critical thinking disposition inventory, schools can monitor the wider progress of their students as they apply their learning skills across the traditional curriculum. Authentic problems call students to apply their critical thinking abilities in new and purposeful ways. As students explain their ideas to one another, they develop communication skills that might not otherwise be nurtured.

Depending on the curriculum being delivered by a school, there may well be an emphasis on building critical thinking abilities in the classroom. Within the International Baccalaureate programs, these life-long skills are often cited in the IB learner profile . Critical thinking dispositions are highly valued in the workplace and this pedagogical approach can be used to harness these essential 21st-century skills.

What are the Benefits of Problem-Based Learning?

Student-led Problem-Based Learning is one of the most useful ways to make students drivers of their learning experience. It makes students creative, innovative, logical and open-minded. The educational practice of Problem-Based Learning also provides opportunities for self-directed and collaborative learning with others in an active learning and hands-on process. Below are the most significant benefits of problem-based learning processes:

• Self-learning: As a self-directed learning method, problem-based learning encourages children to take responsibility and initiative for their learning processes . As children use creativity and research, they develop skills that will help them in their adulthood.
• Engaging : Students don't just listen to the teacher, sit back and take notes. Problem-based learning processes encourages students to take part in learning activities, use learning resources , stay active , think outside the box and apply critical thinking skills to solve problems.
• Teamwork : Most of the problem-based learning issues involve students collaborative learning to find a solution. The educational practice of PBL builds interpersonal skills, listening and communication skills and improves the skills of collaboration and compromise.
• Intrinsic Rewards: In most problem-based learning projects, the reward is much bigger than good grades. Students gain the pride and satisfaction of finding an innovative solution, solving a riddle, or creating a tangible product.
• Transferable Skills: The acquisition of knowledge through problem-based learning strategies don't just help learners in one class or a single subject area. Students can apply these skills to a plethora of subject matter as well as in real life.
• Multiple Learning Opportunities : A PBL model offers an open-ended problem-based acquisition of knowledge, which presents a real-world problem and asks learners to come up with well-constructed responses. Students can use multiple sources such as they can access online resources, using their prior knowledge, and asking momentous questions to brainstorm and come up with solid learning outcomes. Unlike traditional approaches , there might be more than a single right way to do something, but this process motivates learners to explore potential solutions whilst staying active.

Embracing problem-based learning

Problem-based learning can be seen as a deep learning approach and when implemented effectively as part of a broad and balanced curriculum , a successful teaching strategy in education. PBL has a solid epistemological and philosophical foundation and a strong track record of success in multiple areas of study. Learners must experience problem-based learning methods and engage in positive solution-finding activities. PBL models allow learners to gain knowledge through real-world problems, which offers more strength to their understanding and helps them find the connection between classroom learning and the real world at large.

As they solve problems, students can evolve as individuals and team-mates. One word of caution, not all classroom tasks will lend themselves to this learning theory. Take spellings , for example, this is usually delivered with low-stakes quizzing through a practice-based learning model. PBL allows students to apply their knowledge creatively but they need to have a certain level of background knowledge to do this, rote learning might still have its place after all.

Key Concepts and considerations for school leaders

1. Problem Based Learning (PBL)

Problem-based learning (PBL) is an educational method that involves active student participation in solving authentic problems. Students are given a task or question that they must answer using their prior knowledge and resources. They then collaborate with each other to come up with solutions to the problem. This collaborative effort leads to deeper learning than traditional lectures or classroom instruction .

Key question: Inside a traditional curriculum , what opportunities across subject areas do you immediately see?

2. Deep Learning

Deep learning is a term used to describe the ability to learn concepts deeply. For example, if you were asked to memorize a list of numbers, you would probably remember the first five numbers easily, but the last number would be difficult to recall. However, if you were taught to understand the concept behind the numbers, you would be able to remember the last number too.

Key question: How will you make sure that students use a full range of learning styles and learning skills ?

3. Epistemology

Epistemology is the branch of philosophy that deals with the nature of knowledge . It examines the conditions under which something counts as knowledge.

Key question:  As well as focusing on critical thinking dispositions, what subject knowledge should the students understand?

4. Philosophy

Philosophy is the study of general truths about human life. Philosophers examine questions such as “What makes us happy?”, “How should we live our lives?”, and “Why does anything exist?”

Key question: Are there any opportunities for embracing philosophical enquiry into the project to develop critical thinking abilities ?

5. Curriculum

A curriculum is a set of courses designed to teach specific subjects. These courses may include mathematics , science, social studies, language arts, etc.

Key question: How will subject leaders ensure that the integrity of the curriculum is maintained?

6. Broad and Balanced Curriculum

Broad and balanced curricula are those that cover a wide range of topics. Some examples of these types of curriculums include AP Biology, AP Chemistry, AP English Language, AP Physics 1, AP Psychology , AP Spanish Literature, AP Statistics, AP US History, AP World History, IB Diploma Programme, IB Primary Years Program, IB Middle Years Program, IB Diploma Programme .

Key question: Are the teachers who have identified opportunities for a problem-based curriculum?

7. Successful Teaching Strategy

Successful teaching strategies involve effective communication techniques, clear objectives, and appropriate assessments. Teachers must ensure that their lessons are well-planned and organized. They must also provide opportunities for students to interact with one another and share information.

Key question: What pedagogical approaches and teaching strategies will you use?

8. Positive Solution Finding

Positive solution finding is a type of problem-solving where students actively seek out answers rather than passively accept what others tell them.

Key question: How will you ensure your problem-based curriculum is met with a positive mindset from students and teachers?

9. Real World Application

Real-world application refers to applying what students have learned in class to situations that occur in everyday life.

Key question: Within your local school community , are there any opportunities to apply knowledge and skills to real-life problems?

10. Creativity

Creativity is the ability to think of ideas that no one else has thought of yet. Creative thinking requires divergent thinking, which means thinking in different directions.

Key question: What teaching techniques will you use to enable children to generate their own ideas ?

11. Teamwork

Teamwork is the act of working together towards a common goal. Teams often consist of two or more people who work together to achieve a shared objective.

Key question: What opportunities are there to engage students in dialogic teaching methods where they talk their way through the problem?

12. Knowledge Transfer

Knowledge transfer occurs when teachers use their expertise to help students develop skills and abilities .

Key question: Can teachers be able to track the success of the project using improvement scores?

13. Active Learning

Active learning is any form of instruction that engages students in the learning process. Examples of active learning include group discussions, role-playing, debates, presentations, and simulations .

Key question: Will there be an emphasis on learning to learn and developing independent learning skills ?

14. Student Engagement

Student engagement is the degree to which students feel motivated to participate in academic activities.

Key question: Are there any tools available to monitor student engagement during the problem-based curriculum ?

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Classroom Practice

Center for Teaching Innovation

Resource library.

• Getting Started with Establishing Ground Rules
• Sample group work rubric
• Problem-Based Learning Clearinghouse of Activities, University of Delaware

Problem-Based Learning

Problem-based learning  (PBL) is a student-centered approach in which students learn about a subject by working in groups to solve an open-ended problem. This problem is what drives the motivation and the learning. 

Why Use Problem-Based Learning?

Nilson (2010) lists the following learning outcomes that are associated with PBL. A well-designed PBL project provides students with the opportunity to develop skills related to:

• Working in teams.
• Managing projects and holding leadership roles.
• Oral and written communication.
• Self-awareness and evaluation of group processes.
• Working independently.
• Critical thinking and analysis.
• Explaining concepts.
• Self-directed learning.
• Applying course content to real-world examples.
• Researching and information literacy.
• Problem solving across disciplines.

Considerations for Using Problem-Based Learning

Rather than teaching relevant material and subsequently having students apply the knowledge to solve problems, the problem is presented first. PBL assignments can be short, or they can be more involved and take a whole semester. PBL is often group-oriented, so it is beneficial to set aside classroom time to prepare students to   work in groups  and to allow them to engage in their PBL project.

Students generally must:

• Examine and define the problem.
• Explore what they already know about underlying issues related to it.
• Determine what they need to learn and where they can acquire the information and tools necessary to solve the problem.
• Evaluate possible ways to solve the problem.
• Solve the problem.
• Report on their findings.

Getting Started with Problem-Based Learning

• Articulate the learning outcomes of the project. What do you want students to know or be able to do as a result of participating in the assignment?
• Create the problem. Ideally, this will be a real-world situation that resembles something students may encounter in their future careers or lives. Cases are often the basis of PBL activities. Previously developed PBL activities can be found online through the University of Delaware’s PBL Clearinghouse of Activities .
• Establish ground rules at the beginning to prepare students to work effectively in groups.
• Introduce students to group processes and do some warm up exercises to allow them to practice assessing both their own work and that of their peers.
• Consider having students take on different roles or divide up the work up amongst themselves. Alternatively, the project might require students to assume various perspectives, such as those of government officials, local business owners, etc.
• Establish how you will evaluate and assess the assignment. Consider making the self and peer assessments a part of the assignment grade.

Nilson, L. B. (2010).  Teaching at its best: A research-based resource for college instructors  (2nd ed.).  San Francisco, CA: Jossey-Bass. 

New Designs for School 5 Steps to Teaching Students a Problem-Solving Routine

Jeff Heyck-Williams (He, His, Him) Director of the Two Rivers Learning Institute in Washington, DC

We’ve all had the experience of truly purposeful, authentic learning and know how valuable it is. Educators are taking the best of what we know about learning, student support, effective instruction, and interpersonal skill-building to completely reimagine schools so that students experience that kind of purposeful learning all day, every day.

Students can use the 5 steps in this simple routine to solve problems across the curriculum and throughout their lives.

When I visited a fifth-grade class recently, the students were tackling the following problem:

If there are nine people in a room and every person shakes hands exactly once with each of the other people, how many handshakes will there be? How can you prove your answer is correct using a model or numerical explanation?

There were students on the rug modeling people with Unifix cubes. There were kids at one table vigorously shaking each other’s hand. There were kids at another table writing out a diagram with numbers. At yet another table, students were working on creating a numeric expression. What was common across this class was that all of the students were productively grappling around the problem.

On a different day, I was out at recess with a group of kindergarteners who got into an argument over a vigorous game of tag. Several kids were arguing about who should be “it.” Many of them insisted that they hadn’t been tagged. They all agreed that they had a problem. With the assistance of the teacher they walked through a process of identifying what they knew about the problem and how best to solve it. They grappled with this very real problem to come to a solution that all could agree upon.

Then just last week, I had the pleasure of watching a culminating showcase of learning for our 8th graders. They presented to their families about their project exploring the role that genetics plays in our society. Tackling the problem of how we should or should not regulate gene research and editing in the human population, students explored both the history and scientific concerns about genetics and the ethics of gene editing. Each student developed arguments about how we as a country should proceed in the burgeoning field of human genetics which they took to Capitol Hill to share with legislators. Through the process students read complex text to build their knowledge, identified the underlying issues and questions, and developed unique solutions to this very real problem.

Problem-solving is at the heart of each of these scenarios, and an essential set of skills our students need to develop. They need the abilities to think critically and solve challenging problems without a roadmap to solutions. At Two Rivers Public Charter School in Washington, D.C., we have found that one of the most powerful ways to build these skills in students is through the use of a common set of steps for problem-solving. These steps, when used regularly, become a flexible cognitive routine for students to apply to problems across the curriculum and their lives.

The Problem-Solving Routine

At Two Rivers, we use a fairly simple routine for problem solving that has five basic steps. The power of this structure is that it becomes a routine that students are able to use regularly across multiple contexts. The first three steps are implemented before problem-solving. Students use one step during problem-solving. Finally, they finish with a reflective step after problem-solving.

Problem Solving from Two Rivers Public Charter School

Before Problem-Solving: The KWI

The three steps before problem solving: we call them the K-W-I.

The “K” stands for “know” and requires students to identify what they already know about a problem. The goal in this step of the routine is two-fold. First, the student needs to analyze the problem and identify what is happening within the context of the problem. For example, in the math problem above students identify that they know there are nine people and each person must shake hands with each other person. Second, the student needs to activate their background knowledge about that context or other similar problems. In the case of the handshake problem, students may recognize that this seems like a situation in which they will need to add or multiply.

The “W” stands for “what” a student needs to find out to solve the problem. At this point in the routine the student always must identify the core question that is being asked in a problem or task. However, it may also include other questions that help a student access and understand a problem more deeply. For example, in addition to identifying that they need to determine how many handshakes in the math problem, students may also identify that they need to determine how many handshakes each individual person has or how to organize their work to make sure that they count the handshakes correctly.

The “I” stands for “ideas” and refers to ideas that a student brings to the table to solve a problem effectively. In this portion of the routine, students list the strategies that they will use to solve a problem. In the example from the math class, this step involved all of the different ways that students tackled the problem from Unifix cubes to creating mathematical expressions.

This KWI routine before problem solving sets students up to actively engage in solving problems by ensuring they understand the problem and have some ideas about where to start in solving the problem. Two remaining steps are equally important during and after problem solving.

The power of teaching students to use this routine is that they develop a habit of mind to analyze and tackle problems wherever they find them.

During Problem-Solving: The Metacognitive Moment

The step that occurs during problem solving is a metacognitive moment. We ask students to deliberately pause in their problem-solving and answer the following questions: “Is the path I’m on to solve the problem working?” and “What might I do to either stay on a productive path or readjust my approach to get on a productive path?” At this point in the process, students may hear from other students that have had a breakthrough or they may go back to their KWI to determine if they need to reconsider what they know about the problem. By naming explicitly to students that part of problem-solving is monitoring our thinking and process, we help them become more thoughtful problem solvers.

After Problem-Solving: Evaluating Solutions

As a final step, after students solve the problem, they evaluate both their solutions and the process that they used to arrive at those solutions. They look back to determine if their solution accurately solved the problem, and when time permits they also consider if their path to a solution was efficient and how it compares to other students’ solutions.

The power of teaching students to use this routine is that they develop a habit of mind to analyze and tackle problems wherever they find them. This empowers students to be the problem solvers that we know they can become.

Jeff Heyck-Williams (He, His, Him)

Director of the two rivers learning institute.

Jeff Heyck-Williams is the director of the Two Rivers Learning Institute and a founder of Two Rivers Public Charter School. He has led work around creating school-wide cultures of mathematics, developing assessments of critical thinking and problem-solving, and supporting project-based learning.

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The role of critical thinking skills and learning styles of university students in their academic performance

Zohre ghazivakili.

1 Emergency medical services department, Paramedical school, Alborz University of Medical Sciences, Karaj, Iran;

ROOHANGIZ NOROUZI NIA

2 Educational Development Center, Alborz University of Medical Sciences, Karaj, Iran;

FARIDE PANAHI

3 Nursing and midwifery school, Shahid Beheshti University of Medical Sciences, Tehran, Iran;

MEHRDAD KARIMI

4 Department of Epidemiology and Biostatistics, Public Health School, Tehran, Iran;

HAYEDE GHOLSORKHI

5 Medical school, Alborz University of Medical Sciences, Karaj, Iran;

ZARRIN AHMADI

6 Amirkabir University of Technology(Polytechnic), Tehran, Iran

Introduction: The Current world needs people who have a lot of different abilities such as cognition and application of different ways of thinking, research, problem solving, critical thinking skills and creativity. In addition to critical thinking, learning styles is another key factor which has an essential role in the process of problem solving. This study aimed to determine the relationship between learning styles and critical thinking of students and their academic performance in Alborz University of Medical Science.

Methods: This cross-correlation study was performed in 2012, on 216 students of Alborz University who were selected randomly by the stratified random sampling. The data was obtained via a three-part questionnaire included demographic data, Kolb standardized questionnaire of learning style and California critical thinking standardized questionnaire. The academic performance of the students was extracted by the school records. The validity of the instruments was determined in terms of content validity, and the reliability was gained through internal consistency methods. Cronbach's alpha coefficient was found to be 0.78 for the California critical thinking questionnaire. The Chi Square test, Independent t-test, one way ANOVA and Pearson correlation test were used to determine relationship between variables. The Package SPSS14 statistical software was used to analyze data with a significant level of p<0.05.

Results: Our findings indicated the significant difference of mean score in four learning style, suggesting university students with convergent learning style have better performance than other groups. Also learning style had a relationship with age, gender, field of study, semester and job. The results about the critical thinking of the students showed that the mean of deductive reasoning and evaluation skills were higher than that of other skills and analytical skills had the lowest mean and there was a positive significant relationship between the students’ performance with inferential skill and the total score of critical thinking skills (p<0.05). Furthermore, evaluation skills and deductive reasoning had significant relationship. On the other hand, the mean total score of critical thinking had significant difference between different learning styles.

Conclusion: The results of this study showed that the learning styles, critical thinking and academic performance are significantly associated with one another. Considering the growing importance of critical thinking in enhancing the professional competence of individuals, it's recommended to use teaching methods consistent with the learning style because it would be more effective in this context.

Introduction

The current world needs people with a lot of capabilities such as understanding and using different ways of thinking, research, problem solving, critical thinking and creativity. Critical thinking is one of the aspects of thinking that has been accepted as a way to overcome the difficulties and to facilitate the access to information in life ( 1 ).

To Watson and Glizer, critical thinking is a combination of knowledge, attitude, and performance of every individual. They also believe that there are some skills of critical thinking such as perception, assumption recognition deduction, interpretation and evaluation of logical reasoning. They argue that the ability of critical thinking, processing and evaluation of previous information with new information result from inductive and deductive reasoning of solving problems. Watson and Glizer definition of critical thinking has been the basis of critical thinking tests that are widely used to measure the critical thinking today ( 2 ).

World Federation for Medical Education has considered critical thinking one of the medical training standards so that in accredited colleges this subject is one of the key points. In fact, one of the criteria for the accreditation of a learning institute is the measurement of critical thinking in its students ( 3 ).

In addition to critical thinking, learning style, i.e. the information processing method, of the learners, is an important key factor that has a major role in problem solving. According to David Kolb’s theory, learning is a four-step process that includes concrete experience, reflective observation, abstract conceptualization and active experimentation. This position represents two dimensions: concrete experience versus abstract thinking, and reflective observation to active experimentation. These dimensions include four learning styles: divergent, convergent, assimilate, and accommodate. According to Kolb and Ferry, the learner needs four different abilities to function efficiently: Learning styles involve several variables such as academic performance of learner, higher education improvement; critical thinking and problem solving ( 4 ).

Due to the importance of learning styles and critical thinking in students' academic performance, a large volume of educational research has been devoted to these issues in different countries. Demirhan, Besoluk and Onder (2011) in their study on critical thinking and students’ academic performance from the first semester to two years later have found that contrary to expectations the students’ critical thinking level reduced but the total mean of students’ scores increased. This is due to the fact that the students are likely to increase adaptive behavior with environment and university and reduce the stress during their education ( 1 ).

In another study over 330 students in Turkey, the students who had divergent learning style, had lower scores in critical thinking in contrast with students who have accommodator learning style ( 5 ).

Also Mahmoud examined the relationship between critical thinking and learning styles of the Bachelor students with their academic performance in 2012. In this study all the nursing students of the university in the semesters four, six and eight were studied. The results did not show any significant relationship between critical thinking and learning styles of nursing students with their academic performance ( 6 ).

Another research by Nasrabadi in 2012 showed a positive relationship between critical thinking attitudes and student's academic achievement. The results showed that there was a significant difference between the levels of critical thinking of assimilating and converge styles. Also converging, diverging, assimilating and accommodating styles had the highest level of critical thinking, respectively ( 4 ). Among other studies we can refer to Sharma’s study in 2011 whose results suggested a relationship between the academic performance and learning styles ( 7 ).

Today university students should not only think but also should think differently and should not only remember the knowledge in their mind but also should research the best learning style among different learning styles. Therefore, the study on the topic of how the students think and how they learn has received great emphasis in recent years. In this regard, with the importance of the subject, researchers attempted to doa research in this area to determine the relationship between critical thinking and learning styles with academic performance of the students at Alborz University of Medical Sciences.

This study is a descriptive-analytic, cross sectional study and investigates the relationship between critical thinking and learning styles with students’ academic performance of Alborz University of Medical Science in 2012. After approval and permission from university’s authorities and in coordination with official faculties, the critical thinking and learning styles questionnaire was given to the undergraduate students in associate degree, bachelor, medicine (second semester and after that). The total number of participants in the study was 216 students with different majors such as medical, nursing and midwifery, and health and medical emergency students. The tool to collect the data was a two-part questionnaire of Kolb's learning styles and California's critical thinking skills test (form B). The Kolb's questionnaire has two parts. The first part asks for demographic information and the second part includes 12 multiple choice questions. The participants respond to the questions with regard to how they learn, and the scores of respondents are ranked from 1 to 4 in which 4 is most consistent with the participants’ learning style 3 to some extent, 2 poorly consistent and 1 not consistent To find the participants’ learning styles, the first choice of all 12 questions were added together and this was repeated for other choices. Thus, four total scores for the four learning styles were obtained, the first for concrete experience learning style, the second for reflective observation of learning style, the third for abstract conceptualization learning style and the forth for active experimentation learning style. The highest score determined the learning style of the participant. The California critical thinking skills test (form B) includes 34 multiple choice questions with one correct answer in five different areas of critical thinking skills, including evaluation, inference, analysis, inductive reasoning and deductive reasoning. The answering time was 45 minutes and the final score is 34 and the achieved score in each section of the test varies from 0 to 16. In the evaluation section, the maximum point is 14, in analysis section 9, in inference section 11, in inductive reasoning 16 and in deductive reasoning the maximum point was 14. So there were 6 scores for each participant, which included a critical thinking total score and 5 score for critical thinking skills. Dehghani, Jafari Sani, Pakmehr and Malekzadeh found that the reliability of the questionnaire was 78% in a research. In the study of Khalili et al., the confidence coefficient was 62% and construct validity of all subscales with positive and high correlation were reported between 60%-65%. So this test was reliable for the research. Collecting the information was conducted in two stages. In the first stage, the questionnaires were given to the students and the objectives and importance of the research were mentioned. In the next stage, the students' academic performance was reviewed. After data collection, the data were coded and analyzed, using the SPSS 14 ( SPSS Inc, Chicago, IL, USA) software. To describe the data, descriptive statistics were used such as mean and standard deviation for continues variables and frequency for qualitative variables. Chi Square test, Independent t-test, one way ANOVA and Pearson correlation test were used to determine the relationship between variables at a significant level of p<0.05.

Research hypothesis

• There is a relationship between Alborz University of Medical Sciences students’ learning styles and their demographic information. 
• There is a relationship between Alborz University of Medical Sciences students’ critical thinking and their demographic information. 
• There is a relationship between Alborz University of Medical Sciences students’ academic performance and their demographic information. 
• There is a relationship between Alborz University of Medical Sciences students’ learning styles and their academic performance. 
• There is a relationship between Alborz University of Medical Sciences students’ learning styles and their critical thinking. 

225 questionnaires were distributed of which 216 were completely responded (96%). The age range of the participants was from 16 to 45 with the mean age of (22.44±3.7). 52.8% of participants (n=114) were female, 83.3% (n=180) were single, 30.1% of participants’ (n=65) major was pediatric anesthesiology of OR, 35.2% of participants (n=76) were in fourth semester, 74.5% (n=161) were unemployed and 48.6 % (n=105) had Persian ethnicity.

The range of participants’ average grade points, which were considered as their academic performance, were from 12.51 to 19.07 with a mean of (16.75±1.3). According to Kolbs' pattern, 42.7% (n=85) had the convergent learning style (the maximum percentage) followed by 33.2 % (n= 66) with the assimilating style and only 9.5%, (n= 19) with the accommodating style (the minimum percentage).

Among the 5 critical thinking skills, the maximum mean score belonged to deductive reasoning skill (3.38±1.58) and the minimum mean score belonged to analysis skill (1.67±1.08).

Table 1 shows the frequency distribution and demographic variables and the academic performance of the students. According to the Chi-square (Χ 2 ) p-value, there was a significant relationship between gender and learning style (p=0.032), so that nearly 50 percent of males had the assimilating learning style and nearly 52 percent of the females had the convergent learning style.

The relationship between demographic variable and student’s academic performance with learning styles

The relationship between employment, major and semester of studying with the learning style was significant at a p-value of 0.049, 0.006, 0.009 and 0.001, respectively. The mean and standard deviation of age and students' academic performance in the four learning styles are reported in Table 1 .

Using the one way analysis of variance (One way ANOVA) and comparing the mean age of four groups, we found a significant relation between age and academic performance with learning style (p=0.049).

The students with convergent learning style had a better academic performance than those with other learning styles and in the performance of those with the assimilating learning style the weakest.

Table 2 shows the relationship between the total score of critical thinking skills and each of the demographic variables and academic performance. The results of the t-test and one way ANOVA variance analysis are reported to investigate the relationship between each variable with skills below the mean standard deviation.

Relationships between CCT Skills and demographic variables Using t-test and ANOVA. Pearson Correlation coefficient between age and Student's performance with CCT Skills was reported

* Significant in surface 0.05 

** Significant in surface 0.01

Based on the t-test and ANOVA, p-value of t and F, the mean of total score of critical thinking skills had only significant relationship with students’ major (p=0.020). Also a significant relationship was found between the major of students and gender with inference skill; semester of study with deductive reasoning skill, and ethnicity with 2 skills of inference and deductive reasoning (p<0.05).

Also regarding the relationship between age and the student academic performance with each of the critical thinking skills, the Pearson correlation coefficient results indicated a significant positive relationship but a negative relationship between age and analysis skill, i.e. with the increase of age, the score of analysis skill was reduced (p<0.05). Academic performance of the students had a direct significant relationship with critical thinking total score and inference skill; the more the score, the better the academic performance of students (p<0.05).

Table 3 shows the mean and standard deviation of learning styles score in the 4 groups of learning style. Using ANOVA one way ANOVA, the relationship between learning style and critical thinking skills and the comparison of the mean score for each skill in four styles are reported in the last column of the Table 3 .

The Relationship between critical thinking styles with learning styles

Based on the p-value of ANOVA, the mean of evaluation skill and inductive reasoning skill had a significant difference and the relationship between these two skills with learning style was significant (p<0.05). Also the mean of critical thinking’s total score was significantly different in the four groups and the relationship between total score with learning style was significant, too (p<0.05).

The mean and confidence interval of university students’ performance in four learning  styles

The mean and confidene interval of critical thinking skills

The study findings showed that the popular learning style among the students was the convergent style followed by the assimilating style which is consistent with Kolb's theory stating that medical science students usually have this learning style ( 8 ). This result was consistent with the results of other studies ( 9 , 10 ). In Yenice's study in which the student of training teacher were the target of the project, the most frequent learning styles were divergent and assimilating styles and these differences originate from the different target group of study in 2012 ( 11 ).

This study showed a significant relationship between learning style and gender, age, semester and employment. Meyari et al. did not find any significant relationship between learning style, age and gender of the freshman but for the fifth semester students, a significant relationship with age and gender was found ( 10 ). Also in Yenice's study, no relationship with learning style, gender, semester and age was found.

Furthermore, in the first semester divergent style, in the second semester assimilating style and in the third and fourth semester divergent style were accounted for the highest percentage. Also in the group age of 17-20 years the assimilating style and the age of 21-24 years the divergent style were dominant styles ( 11 ).

In the present study, it was found a significant positive relationship between convergent learning style and academic performance. Also in the study of Pooladi et al. the majority of the students had convergent style and they also found a significant relationship between learning style, total mean score and the mean of practical courses ( 12 ). Nasrabadi et al. found that students with the highest achievement were those with convergent style with a significant difference with those with divergent style ( 4 ). But the results are inconsistent to Meyari et al.’s ( 10 ).

In this study, the obtained mean score from the critical thinking questionnaire was (7.15±2.41) that was compared with that in the study of Khalili and Hoseinzadeh which was to validate and make reliable the critical thinking skills questionnaire of California (form B) in the Iranian nursing students; the mean of total score was about the 11th percentile of this study ( 13 ).

In other words, the computed score for critical thinking of the students participating was lower than 11 score that is in the 50th percentile and of course is lower than normal range.

Hariri and Bagherinezhad had shown that the computed score for Bachelor and Master students of Health faculty was also lower than the norm in Iran ( 14 ). Also Mayer and Dayer came to a similar conclusion in critical thinking skill in the Agricultural university of Florida’s students in 2006 ( 15 ).

But in Gharib et al.’s study, the total score of critical thinking test among the freshman and senior of Health-care management was in normal range ( 16 ). Wangensteen et al., found that the critical thinking skills of the newest graduate nursing students were relatively high in Sweden in 2010 ( 17 ).

In this study, students of all levels (Associate, Bachelor and PhD) with various fields of study participated but other studies have been limited to certain graduate courses that may explain the differences in levels of special critical thinking skills score in this study. In this study we found a significant relationship between total score of critical thinking and major of the students. This result is consistent with Serin et al. ( 18 ).

It was found a significant relationship between major of participants, gender and inference skill, semester and deductive reasoning skill, ethnicity and both inference and deductive reasoning skills.

In the Yenice's study significant relationship between critical thinking, group of age, gender and semester was seen ( 11 ). In Wangensteen et al.’s ( 17 ) study in the older age group, the level of critical thinking score increased. In Serin et al.’s ( 18 ) study the level of communication skills in girls was better than that in boys. And also a significant relationship was found between critical thinking and academic semester, but in Mayer and Dayer’s study no significant relationship between critical thinking levels and gender was found ( 4 , 15 ).

The results also showed that the total score of critical thinking and analytical skills of students and their performance had a significant relationship. Nasrabady et al.’s study also showed that there was a positive relationship between critical thinking reflection attitude and academic achievement ( 4 ). This is contradictory with what Demirhan, Bosluk and Ander found ( 6 , 15 ).

The results of the relationship between learning style and critical thinking indicated that the relationship between evaluation and inductive reasoning was significant to learning style (p<0.05). The relationship of critical thinking total score with learning style was also significant (p<0.05). Thus the total score for those with the conforming style of critical skills was more than that with other styles. But in the subgroup of inference skills, those with the convergent style had a higher mean than those with other styles.

Yenice found a negative relationship between critical thinking score and divergent learning style and a positive relation between critical thinking score and accommodating style ( 11 ).

Siriopoulos and Pomonis in their study compared the learning style and critical thinking skills of students in two phases: at the beginning and end of education and came to this conclusion that the learning style of students changed in the second phase.

For example, the divergent, convergent and accommodating styles languished and the assimilating style (combination of abstract thinking and reflective observation) was noticeably strengthened. However, those with converging learning style had higher levels of critical thinking.

The level of students’ critical thinking was lower in all international standards styles. Perhaps it was because of widely used teacher-centered teaching methods (lectures) in that university ( 19 ).

The results in the study of Nasrabady et al. showed that there was a significant difference between the level of learners’ critical thinking and divergent and assimilating styles ( 4 ).

Those with converging, diverging, assimilating and accommodating styles had the highest level of critical thinking, respectively.

Also there was a positive significant relationship between the reflective observation method and critical thinking and also a negative significant relationship between the abstract conceptualization method and critical thinking ( 4 ). But in another study that Mahmud has done in 2012, he did not find any significant relationship between learning style, critical thinking and students’ performance ( 6 ).

The results of this study showed that the students’ critical thinking skills of this university aren't acceptable. Also learning styles, critical thinking and academic performance have significant relationship with each other. Due to the important role of critical thinking in enhancing professional competence, it is recommend using teaching methods which are consistent with the learning styles.

Acknowledgment

This study is based on a research project that was approved in Research Deputy of Alborz University of Medical sciences. We sincerely appreciate all in Research Deputy of Alborz University of Medical sciences who supported us financially and morally and all students and colleagues who participated in this study.

Conflict of Interest: None declared.

References:

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3 Simple Strategies to Improve Students’ Problem-Solving Skills

These strategies are designed to make sure students have a good understanding of problems before attempting to solve them.

Research provides a striking revelation about problem solvers. The best problem solvers approach problems much differently than novices. For instance, one meta-study showed that when experts evaluate graphs , they tend to spend less time on tasks and answer choices and more time on evaluating the axes’ labels and the relationships of variables within the graphs. In other words, they spend more time up front making sense of the data before moving to addressing the task.

While slower in solving problems, experts use this additional up-front time to more efficiently and effectively solve the problem. In one study, researchers found that experts were much better at “information extraction” or pulling the information they needed to solve the problem later in the problem than novices. This was due to the fact that they started a problem-solving process by evaluating specific assumptions within problems, asking predictive questions, and then comparing and contrasting their predictions with results. For example, expert problem solvers look at the problem context and ask a number of questions:

• What do we know about the context of the problem?
• What assumptions are underlying the problem? What’s the story here?
• What qualitative and quantitative information is pertinent?
• What might the problem context be telling us? What questions arise from the information we are reading or reviewing?
• What are important trends and patterns?

As such, expert problem solvers don’t jump to the presented problem or rush to solutions. They invest the time necessary to make sense of the problem.

Now, think about your own students: Do they immediately jump to the question, or do they take time to understand the problem context? Do they identify the relevant variables, look for patterns, and then focus on the specific tasks?

If your students are struggling to develop the habit of sense-making in a problem- solving context, this is a perfect time to incorporate a few short and sharp strategies to support them.

3 Ways to Improve Student Problem-Solving

1. Slow reveal graphs: The brilliant strategy crafted by K–8 math specialist Jenna Laib and her colleagues provides teachers with an opportunity to gradually display complex graphical information and build students’ questioning, sense-making, and evaluating predictions.

For instance, in one third-grade class, students are given a bar graph without any labels or identifying information except for bars emerging from a horizontal line on the bottom of the slide. Over time, students learn about the categories on the x -axis (types of animals) and the quantities specified on the y -axis (number of baby teeth).

The graphs and the topics range in complexity from studying the standard deviation of temperatures in Antarctica to the use of scatterplots to compare working hours across OECD (Organization for Economic Cooperation and Development) countries. The website offers a number of graphs on Google Slides and suggests questions that teachers may ask students. Furthermore, this site allows teachers to search by type of graph (e.g., scatterplot) or topic (e.g., social justice).

2. Three reads: The three-reads strategy tasks students with evaluating a word problem in three different ways . First, students encounter a problem without having access to the question—for instance, “There are 20 kangaroos on the grassland. Three hop away.” Students are expected to discuss the context of the problem without emphasizing the quantities. For instance, a student may say, “We know that there are a total amount of kangaroos, and the total shrinks because some kangaroos hop away.”

Next, students discuss the important quantities and what questions may be generated. Finally, students receive and address the actual problem. Here they can both evaluate how close their predicted questions were from the actual questions and solve the actual problem.

To get started, consider using the numberless word problems on educator Brian Bushart’s site . For those teaching high school, consider using your own textbook word problems for this activity. Simply create three slides to present to students that include context (e.g., on the first slide state, “A salesman sold twice as much pears in the afternoon as in the morning”). The second slide would include quantities (e.g., “He sold 360 kilograms of pears”), and the third slide would include the actual question (e.g., “How many kilograms did he sell in the morning and how many in the afternoon?”). One additional suggestion for teams to consider is to have students solve the questions they generated before revealing the actual question.

3. Three-Act Tasks: Originally created by Dan Meyer, three-act tasks follow the three acts of a story . The first act is typically called the “setup,” followed by the “confrontation” and then the “resolution.”

This storyline process can be used in mathematics in which students encounter a contextual problem (e.g., a pool is being filled with soda). Here students work to identify the important aspects of the problem. During the second act, students build knowledge and skill to solve the problem (e.g., they learn how to calculate the volume of particular spaces). Finally, students solve the problem and evaluate their answers (e.g., how close were their calculations to the actual specifications of the pool and the amount of liquid that filled it).

Often, teachers add a fourth act (i.e., “the sequel”), in which students encounter a similar problem but in a different context (e.g., they have to estimate the volume of a lava lamp). There are also a number of elementary examples that have been developed by math teachers including GFletchy , which offers pre-kindergarten to middle school activities including counting squares , peas in a pod , and shark bait .

Students need to learn how to slow down and think through a problem context. The aforementioned strategies are quick ways teachers can begin to support students in developing the habits needed to effectively and efficiently tackle complex problem-solving.

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Problem solving styles.

Knowledge of style is important in education in a number of ways. It contributes to adults’ ability to work together effectively in teams and in large groups.  It provides information that helps educators understand their own personal strengths and how to put them to work as effectively as possible across many tasks and challenges. It helps educators communicate more effectively with each other, but also with parents, community members, and, of course, with students.  In addition to its importance for adults, style can also be important in designing and differentiating instruction.

Problem Solving Styles: Free resources

pdf_icon_freeHere you will find a number of free resources that will explain more about problem-solving style, to obtain articles that deal with both research and practice, and to obtain a bibliography to give you direction for future reading and study.

Online Resources

pdf_iconWe provide advanced online resources in PDF format to help you understand the VIEW model of problem-solving style and its implications. These resources are available at a reasonable cost for immediate download.  The cost of each one includes permission to duplicate the file for up to three other individuals at no additional charge.

Print Resources

book_iconOur book, An Introduction To Problem-Solving Style by Donald J. Treffinger, Edwin C. Selby, Scott G. Isaksen, and James H. Crumel, provides a concise, practical overview of problem-solving style. It outlines the nature of problem-solving style (based on extensive theory and research), explaining in clear, non-technical language what problem-solving styles are - and are not - and describes three problem-solving style dimensions and six styles. The book explains the important and unique personal characteristics and implications, benefits, and risks of each style.

In addition, the book discusses: the implications of style for effective problem solving; the importance of style for group composition, teamwork, and enhancing work relationships; and, the unique ways the three style dimensions interact with each other. This book is a valuable resource for building self-understanding and for all teams, groups, or organizations that are concerned with effective leadership, teamwork, solving problems, and managing change.

Problem Solving Style Workshops

workshop_iconOur VIEW programs and services are custom-tailored to meet your needs and interests. We will confer with you, create a complete proposal to meet your unique needs, and work closely with you to carry out our collaborative plan.

We believe that all people have strengths and talents that are important to recognize, develop, and use throughout life.  Read more.

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Mathematical Problem-Solving Style and Performance Of Students

• Luvie Jhun S. Gahi
• Ronald E. Almagro
• Richie Ryan C. Sudoy
• Dec 21, 2023

Mathematical Problem-Solving Style and Performance of Students

1 Luvie Jhun S. Gahi, 2 Ronald E. Almagro, 3 Richie Ryan C. Sudoy

1 Student, Master of Arts in Education Major in Mathematics, St. Mary’s College of Tagum,

2 Student, Master of Arts in Education Major in English, St. Mary’s College of Tagum,

3 Student, Doctor of Philosophy in Educational Management, Davao del Norte State College, Philippines

DOI: https://dx.doi.org/10.47772/IJRISS.2023.7011142

Received: 10 October 2023; Revised: 17 November 2023; Accepted: 20 November 2023; Published: 21 December 2023

This study aimed to determine whether the mathematical problem-solving style significantly affects the students’ performance in which a descriptive-correlational research design was used. Through stratified sampling, there were 291 first-year college respondents in the local college in Sto. Tomas, Davao del Norte who were chosen. This study used one adapted questionnaire and one researchers-made questionnaire with Mean, Pearson r, Standard Deviation, T-test, and Analysis of Variance used as the statistical tools. The students’ mathematics attained very good performance with the mathematical problem-solving style of students’ sensing, intuition, feeling, and thinking moderately observed. The findings revealed that the mathematical problem-solving style has a significant relationship with Students’ performance. However, there is a significant difference in the mathematical problem-solving style of students when grouped according to various programs. The result also revealed that there is no significant difference in the mathematical problem-solving style of students when grouped according to sex (male and female).  Students, instructors, college administrators, and Commission of Higher Education officials (CHED) are encouraged to value the importance of mathematical problem-solving style in the performance of the students. Instructors, college administrators, and CHED officials must establish programs that will enhance the mathematical problem-solving styles and performance of students. College instructors and administrators must work collaboratively to achieve better performance in the mathematics. Therefore, these should ensure that the necessary materials, resources, activities, and differentiated instruction are provided and used to meet the students’ needs to learn and to encourage in the problem-solving style.

Keywords: Student’s Profile, Mathematical Problem-Solving Style, and Performance of Students

INTRODUCTION

Good problem-solving abilities are required for all issues emerging from daily activity or to progress through the developmental stages. Effective problem-solving style has been linked to beneficial psychological outcomes such as competence, productivity, and optimism (Carver & Scheier, 1999; Chang & D’Zurilla, 1996; Elliott, et al., 1994). Additionally, according to the National Council of Teachers of Mathematics (NCTM), problem-solving ability is an essential component of all mathematics learning. The ability to solve problems can provide significant benefits in everyday life and the workplace. However, problem-solving is not only a goal of learning mathematics, but also a major method of learning mathematical concepts.

Likewise, the process of problem-solving begins with the observation of a gap, the application, and the complete evaluation of a theory to close that loophole. Styles of problem-solving are viewed as contrasting individuals’ unique characteristics with the behaviors that people prefer to draw and concentrate on their efforts to arrive at some comprehension or awareness, generate ideas, and make plans for the work(Sutherland, 2002).In the local college of Sto. Tomas, Davao del Norte, the varying levels of student performance in mathematics courses was used in this study. Surprisingly, there seemed to be a gap in the published research addressing this issue (Almagro etal., 2023)

The ability and style to solve problems increase the students’ comfort level when solving mathematical problems and practical difficulties. In turn, having the ability to solve problems has several advantages. For instance, problem-solving style is a feature of mathematical activity and a key method for developing mathematical understanding (NCTM, 2000). This statement implies that problem-solving style is an essential component of mathematics education. Furthermore, students learn to apply their mathematical skills in various ways; they gain a deeper understanding of mathematical concepts and gain firsthand experience as a mathematician by solving problems (Badger et al., 2012). Consequently, instruction should be advanced to enable the students to recognize and address the issues they encountered in real-life situations (Phonapichat et al., 2014). Nevertheless, several research findings suggest that children struggle to solve problems because of (Herawatty et al., 2018). Thus, learning mathematics should encourage students to solve problems confidently using mathematics. Learning mathematics in school should assist the students in understanding and applying mathematics to their problems that occur in their daily lives and in the workplace. The learning program must enable students to develop new mathematical knowledge through problem-solving style, solve mathematics and other problems, implement and adjust various problem-solving strategies, and monitor and reflect on the problem-solving style (NCTM, 2000).

The existing literature acknowledges challenges in mathematical problem-solving, but there is a significant gap in understanding the specific difficulties students face and the effectiveness of different problem-solving strategies. GanzonandEdig (2022), recognize the challenges, there is a need for in-depth investigations into the categories of difficulties encountered during the problem-solving process, low academic performance during in the pandemic. Additionally, the literature notes the importance of problem-solving models (Foshay & Kirkley, 2003; Almagro & Edig,2023), but a gap exists in understanding the comparative effectiveness of these motivated learning strategies. Moreover, within Realistic Mathematics Education (RME), recognized for its real-world emphasis, there is a need to examine the specific contextual factors that contribute to or hinder students’ success in mathematical problem-solving. Addressing these gaps will provide valuable insights for supporting students in developing effective problem-solving skills in mathematics.

The objective of this study is to investigate the relationship between mathematical problem-solving styles and the performance of students. Specifically, it aims to identify the various problem-solving styles employed by students, explore the challenges they face in mathematical problem-solving, and assess the impact of different problem-solving strategies on overall performance. The study seeks to contribute valuable insights that can inform educational practices and enhance students’ proficiency in mathematical problem-solving.

Statement of the Problem

The purpose of this study was to determine the relationship between mathematical problem-solving style and performance of first-year college students in Sto. Tomas College of Agriculture, Science and Technology (STCAST)in the academic year 2022-2023.

Specifically, these research questions sought to answer the following:

1. What is the measurable level of students’ performance in solving math problems? 2. What is the quantifiable level of students’ mathematical problem-solving style, considering the dimensions of sensing, intuition, feeling, and thinking? 3. Is there a significant and measurable relationship between students’ mathematical problem-solving style and their performance? 4. Can the mathematical problem-solving style of students be significantly differentiated when grouped according to sex and program, making it a specific and measurable analysis? 5. What specific and measurable instructional interventions can be proposed based on the study’s results, ensuring relevance and time-bound applicability?

The following hypotheses was tested at a 0.05 level of significance. Specifically, this was drawn to determine whether mathematical problem-solving style of students differ in terms of their sex and program.

• There is no significant relationship between the mathematical problem-solving style and performance of students.
• There is no significant difference on the mathematical problem-solving style of the students when classified according to sex and programs.

Theoretical Framework

The study is grounded in the original problem-solving style model, rooted in the concept of psychological functions as proposed by Jung (1923) and further developed by Moon (2008) and Taylor & Mackenny (2008). This model encompasses thinking, feeling, sensation, and intuition as the four psychological functions (Ghodrati et al., 2014). Building upon this foundation, the research draws attention to gender-specific problem-solving tendencies, with Burkey and Miller (2005) finding that women often employ intuition in work settings, contrasting the emphasis on rational problem-solving linked to masculinity by Wang, Heppner, and Berry (2007). Additionally, Conner (2000) identifies women as more intuitive global thinkers, emphasizing simultaneous, interconnected processing of information. The study aligns with the belief that students’ problem-solving methods significantly influence their academic achievement and success (Poshtiban, 2007; Morton, 2001).

Conceptual Framework

The study’s conceptual paradigm, which is shown in Figure 1, summarizes the variables which composed of mathematical problem-solving style and performance of students. On the one hand, the independent variable consists of mathematical problem-solving style which includes the indicators of sensing, intuitive, feeling, and thinking. On the other hand, the dependent variable consists of the performance of students in mathematics which composed of the moderating variable, i.e., the student respondent’s profile which are classified into sex and program. The researchers’ interpretation explains what the study wants to achieve as emphasized in the figure below. As such, the study aims to assess the Mathematical Problem-Solving Style of College students, and its relationship to their performance, and produce Students’ Instructional Intervention Plan that will improve their styles in solving mathematical problems.

 Figure 1. The Conceptual Paradigm of the Study

METHODOLOGY

This section covers the study’s numerous methodologies, which include the research design, respondents, research instrument, data gathering procedures, statistical treatment of data, and ethical considerations.

Research Design

This study employed descriptive and correlational study design. Descriptive research entails gatherings of quantitative data that may be tabulated along a scale in numerical forms, such as test scores. It entails collecting data by describing occurrences and then arranging, tabulating, displaying, and summarizing the data (Glass & Hopkins, 1984). The researcher will utilize this design to determine and describe the variables employed in this study. It utilized the mean test since this aimed to measure the level of performance of students.

Correlational study, meanwhile, tried to establish correlations between two or more variables. It looked to see if a rise or drop in one variable corresponded to an increase or decrease in another (Tan, 2014). This design will be utilized by the researcher to examine and determine the existing correlations between the variables in this research.

This study was concerned with data collection utilizing adopted research instrument and a pilot-tested researchers’ made examination to evaluate the hypotheses whether the mathematical-problem solving style influences the student performance. It will test the data using the proper statistical tools. Furthermore, the study’s major objective is to distinguish between the mathematical problem-solving styles of students when they are classified by sex and program. Thus, the study intends to look into the relationship between mathematical problem-solving style and performance of freshmen students in various programs at Sto. Tomas College of Agriculture, Sciences, and Technology.

Participants of the Study

The respondents of this research were the first-year college students enrolled in bachelor of Technical and Vocational Teacher Education (BTVTED), Bachelor of Science in Agricultural Business (BSAB), Bachelor of Science in Office Administration (BSOA), and Bachelor of Public Administration (BPA)programs for the school year 2022-2023. The respondents’ total population size of this study comprises of 1,188 students coming from four (4) programs in Sto. Tomas College of Agriculture, Sciences and Technology (STCAST). Specifically, BSOA department consists of 426 first-year students, BSAB department consists of 381 first-year students, BPA department consists of 217 first-year students, and BTVTED department consists of 164 first-year students. By using Qualtrics online sample size calculator, given the identified collective population size of 1,188 students, the ideal sample size of this quantitative study will consist of 291 students in total.

Moreover, this study utilized a stratified sampling technique to determine the sample size and determine the final total number of respondents. As a result, the BSOA program has an ideal sample of 176 students, BSAB program with 94 students, BPA with 53 students, and BTVTED with 40 students.

Materials/Research Instrument

One adapted research instrument and one researcher-made examination were used in this study. This was selected and modified to match the overall objectives of the study. These research instruments were validated by a panel of experts.

Problem-Solving Style Questionnaire (PSSQ). This instrument contains a 20-item survey questionnaire comprising the six (4) components problem-solving in mathematics such as Sensing (5 items), Intuitive (5 items), Feeling (5 items), and Thinking (5 items). This questionnaire was anchored on a 5-point Likert scale ranging from 5 as strongly agree to 1 as strongly disagree.

The following parameter limits, with its corresponding descriptions, were applied for the level of students’ mathematical problem-solving style.

The instrument for performance of students in Mathematics was a pilot-tested researcher-made questionnaire worth 40-item questionnaire. This instrument had been determined to possess good psychometric validity and reliability. The value of Cronbach’s a for the total scale is 0.747. All items of problem-solving skills are acceptable.

The percentage of the test score was computed by dividing the number of correct responses over the total highest possible score by multiplying it by 70 add 30. The highest probable score to achieve will be 40.

For the level of the mathematical problem-solving skills, the following parameter was used.

Data Gathering Procedure

The necessary data was gathered in a systematic procedure, which will involve the following.

Seeking permission to conduct the study. The researcher sought approval to conduct the research project. Primarily, the researcher will acquire a letter of recommendation from the College President of Santo Tomas College of Agriculture, Sciences, and Technology (STCAST). After acceptance, the researcher submitted a copy of the recommendation to the respective Department Heads of the following four (4) Degree Programs such as Bachelor of Science in Agriculture and Business (BSAB), Bachelor of Technical Vocational Teacher Education (BTVTED), Bachelor of Science in Office Administration (BSOA), and Bachelor of Public Administration (BPA) to finalize the approval to conduct the entire study.

General orientation and seeking of consent from research respondents . The study’s conduct was to regulate by ethical values, i.e., respect for individuals, beneficence, and justice, particularly in terms of data privacy and protection. Prior to data collection, the researcher will generate informed consent or assent forms and request them from respondents by e-mail. As evidence of their voluntary involvement in the full study, all forms will be delivered and signed electronically by those research respondents through e-mail message.

In addition, the researcher gave a brief 30-minute virtual presentation about the findings to the respondents. In accordance to this procedure, respondents who confirmed their voluntary involvement in the study were given a unique connection to Google Classroom developed by the researcher where the participants can partake in a brief virtual presentation. This was done specifically before conducting the survey. However, those respondents who were unable to attend the orientation due to unforeseen circumstances or personal reasons were educated about the research by phone call or chat through Facebook Messenger by the researcher. In addition, all respondents received a recorded video from the virtual presentation, which can be observed within the Google Classroom built by the researcher for the research.

Administration and retrieval of the questionnaire . The study took place in February of the school year 2022-2023. In order to carry out the research, the researcher will first develop Google Forms that will be utilized to collect responses from respondents based on the survey questions from the questionnaires. The quantitative data for this study was collected online using Google Forms. The researcher managed all direct contact and administration of surveys to respondents.

All surveys were allotted within one 90-minute session commencing with the Mathematical Problem-Style Questionnaire (MPSQ) and researcher-made examination. To protect the data, respondents was required to take the survey at a location and on a technological device (e.g., laptop, cellphone, or tablet) where only they have access to those offered online survey surveys via Google Forms. This was explicitly stated in their Informed Consent Form (ICF). In addition, the data questionnaire was returned to the researcher on time. Furthermore, the researcher handled personal communication and questionnaire administration. Finally, questionnaires were administered when the respondents’ 90-minute session has expired.

Checking, collating, and processing of data. The researcher gathered, validated, and quantified the respondents’ scores collected in an Excel file throughout this step. Following the tabulation, the data were submitted to an expert or certified statistician for data analysis. The researcher analyzed the results based on the data analysis for specific discoveries, discussions, and conclusions. This was accomplished mostly through data table and graphical presentations. Furthermore, descriptive statements were used to further explain and easily grasp the findings in relation to the study’s variables.

Statistical Tool for Data Analysis

The study’s findings were examined and comprehended properly using statistical methods such as Mean, Standard Deviation, Pearson r, T-test, and Analysis of Variance (ANOVA).

Mean . This method of analysis was used to measure the level of performance of students and their mathematical problem-solving style. Specifically, this was addressed in the first and second research questions.

Standard Deviation . A standard deviation is a statistical measure of the dispersion of a dataset in reference to its mean. This kind of analysis was used to determine how widely scattered the data is or how close the scores are to the mean. This was specifically answer the first and second research questions.

Pearson r. This statistical analysis was utilized to establish the existence of a significant relationship between mathematical problem-solving style and the performance of students. This will be utilized to specifically address the third research question.

T-test. This statistical analysis was utilized to determine if there was significant difference in the mathematical problem-solving style of students when classified into sex. This will specifically answer the fourth research question.

Analysis of Variance . This statistical analysis was utilized to determine if there was a significant difference in the mathematical problem-solving style of students when classified into four (4) different programs.

RESULTS AND DISCUSSION

In this chapter, the researchers present the results and discussions from the data gathered. In particular, this shows the data in tables and its corresponding descriptive interpretations.

Level of Mathematical Problem-Solving Style of Students in terms of Sensing

Table 1 presents the level of Mathematical Problem-Solving Style of Students in terms of Sensing. The item “As a student, I like to solve math problems and I am comfortable to trying to learn new skills.” has the highest mean of 3.69 with a descriptive equivalent of high. This is followed by the item “Before I put energy into solving math problems, I want to know first the benefits I can get from it.”, with a mean of 3.62 and high descriptive equivalent. On the contrary, the item “I tend to focus on immediate problems and let others worry about the distant future.” with the lowest mean of 3.20 and descriptive equivalent of moderate.

Table 1 Level of Mathematical Problem-Solving Style of Students in terms of Sensing

Furthermore, it has a category mean of 3.42 with descriptive equivalent of high. This indicates that the mathematical problem-solving style of students in terms of sensing is observed. Moreover, it has an Standard Deviation (SD) of 0.97.

The dispersion of the mathematical problem-solving style of students in terms of sensing based on the answers of the students revealed that the SD is 0.97. This indicates that the measures of variability of sensing as a mathematical problem-solving style of students are near the mean.

The result shows that students are interested in solving math problems and they are comfortable in trying new skills. It is also much observed that before solving math problems, students want to identify first the benefits they can get from it. Furthermore, students focus on immediate problems. Moreover, Vicente et al. (2002) observed that individuals with a sensation-type problem-solving style tend to focus on details and gather specific, factual data from their environment using their five senses. This approach involves a preference for concrete, practical, and tangible information, rather than abstract or theoretical concepts. These individuals tend to use a step-by-step approach to problem-solving, relying on established rules and procedures, and often prefer to work with real-world problems that have clear and immediate applications. Similarly, the study of Hsieh and Lin (2006) investigated the connection between mathematical problem-solving style and sensory preference among high school students. The study established that students with a sensing preference tended to use a more practical, sequential, and concrete problem-solving approach.

Level of Mathematical Problem-Solving Style of Students in terms of Intuitive

Table 2 presents the level of Mathematical Problem-Solving Style of Students in terms of Intuitive. The item “ As a student, I solve math problems accurately by knowing all the details of the problem.” has the highest mean of 3.68 with a descriptive equivalent of high. This is followed by the item “As a student, I enjoy solving mathematical problems.” with a mean of 3.36 and a descriptive equivalent of moderate. On the contrary, the item “ As a student, I solve mathematical problems quickly without wasting a lot of time on details.” has a mean of 3.05 with a descriptive equivalent of moderate.

Table 2 Level of Mathematical Problem-Solving Style of Students in terms of Intuitive

Furthermore, it has a category mean of 3.26 with a descriptive equivalent of moderate. It means that the mathematical problem-solving style of students in terms of intuitive is moderately observed. Moreover, the standard deviation of 1.01 in the category mean indicates that the measures of the variability of the mathematical problem-solving style of students in terms of intuition are near the mean.

It is observed that the students solve math problems accurately by knowing all the details of the problem. Additionally, it is moderately observed that students enjoy solving mathematical problems quickly and without wasting a lot of time on details. Hafriani (2018) suggests that students rely heavily on their intuition when it comes to solving mathematical problems. Students who use intuitive thinking to solve mathematical problems exhibit several characteristics: directness, self-evidence, intrinsic certainty, perseverance, coercion, extrapolation, globality, and implicitness.

Similarly, in the study conducted by Wuryanieet al., (2020) they found that students tend to rely on intuition when solving problems, exhibiting traits such as directness, self-evidence, extrapolation, intrinsic certainty, coercion, and decisiveness.

Level of Mathematical Problem-Solving Style of Students in terms of Feeling

Table 3 presents the level of Mathematical Problem-Solving Style of students in terms of feeling. The item “I want to solve math problems within a group and not individually.” has the highest mean of 3.73 with a descriptive equivalent of high. This is followed by the item “As a student, I can tell how others feel about solving math problems.” with a mean of 3.60 and a descriptive equivalent of high. On the other hand, the item “I try to please others and need occasional praise for myself.” has the lowest mean of 3.05 with a descriptive equivalent of moderate.

Table 3 Level of Mathematical Problem-Solving Style of Students in terms of Feeling

Moreover, it has a category mean of 3.48 with a descriptive equivalent of high. It implies that the mathematical problem-solving style of students in terms of feeling is observed. Consequently, the standard deviation of 0.98 in the category mean indicates that the measures of variability of the mathematical problem-solving style of students in terms of feeling are close to the mean.

Based on the results, it is observed that students want to solve math problems within a group and not individually. In addition, it is also observed that students can tell how others feel about solving math problems. It is moderately observed that in solving math problems, students try to please others and need occasional praise for their selves. A study by Goez et al. (2005) found that students must gain information and abilities related to feelings. Moreover, Altun (2003) examined the students who tend to rely on their feelings when solving problems prioritize their emotional and personal approaches in the problem-solving process. Along with this, Ahmed et al. (2014) stated that the mathematical problem-solving style has a favorable effect on the student’s attention, motivation to learn, choice of learning tools, self-regulation of learning, and academic performance.

Level of Mathematical Problem-Solving Style of students in terms of Thinking

Table 4 presents the level of mathematical problem-solving style of students in terms of thinking. The item “ As a student, I don’t let mathematics word problems discourage me, no matter how difficult they are.” has the highest mean of 3.65 with a descriptive equivalent of high. This is followed by the item “I solve math problems by analyzing all the facts and putting them in systematic order.” with a mean of 3.56 with a descriptive equivalent of high. On other hand, the item “ When I have a math problem to be solved, I solve it, even if others’ feelings might get hurt in the process.” has the lowest mean of 3.08 with a descriptive equivalent of moderate.

Furthermore, it has a category mean of 3.36 with a descriptive equivalent of moderate. This implies that the mathematical problem-solving style of students in terms of intuitive is moderately observed. Consequently, the standard deviation of 1.01 in the category means indicates that the measures of variability of the mathematical problem-solving style of students in terms of intuition are close to the mean.

Table 4 Level of Mathematical Problem-Solving Style of Students in terms of Thinking

Based on the results, it is observed that students approach mathematics word problems in a thoughtful and analytical manner. They are not easily discouraged by the difficulty of the problems and instead persevere until they find a solution. Students employ a systematic approach to problem-solving, carefully considering all the relevant facts and putting them in a logical order. This approach aligns with the findings of Khan et al. (2016), who emphasize the importance of analysis and research in effective problem-solving. Additionally, students demonstrate a commitment to objectivity and impartiality, seeking solutions that are grounded in evidence and reason. This aligns with the notion that mathematical problem-solving requires a high level of reflective thinking, as highlighted by Kneeland (2001) and Macaso and Dagohoy (2022). Moreover, the results of this study suggest that students are generally well-equipped to tackle mathematics word problems. They possess the necessary skills and mindset to approach these problems in a thoughtful, analytical, and objective manner. Thinking based on a mental process is an essential component of problem-solving solving, and problem-solving abilities are dependent on the correct application of thinking and solution processes. Furthermore, high-level thinking skills are involved in the intricate process of problem-solving (Gürsan & Yazgan, 2020).

Summary of the Level of Mathematical-Problem Solving Style of Students

Table summarizes the level of mathematical problem-solving style of students. Among the four indicators, “feeling” acquire the highest mean of 3.48 with descriptive equivalent of high. “Sensing” developed a mean of 3.42 with a descriptive equivalent of high. They have an SD of 0.98 and 0.97, respectively. It is followed by “thinking” with a mean of 3.36 with a descriptive equivalent of moderate and an SD of 1.01. On other hand, “intuitive” got the lowest mean of 3.26 with a descriptive equivalent of moderate and an SD of 1.01.

Table 5 Summary of the Level of Mathematical-Problem Solving Style of Students

Furthermore, it has an overall mean of 3.38 with a descriptive equivalent of moderate. This means that the mathematical problem-solving style of students is moderately observed. The findings suggest that there is a high degree of homogeneity in the mathematical problem-solving styles of the students, as evidenced by the small standard deviation of 0.99 in the overall mean. This indicates that the measures of variability in the students’ responses are clustered closely around the mean. Such a narrow range of variability in the problem-solving styles implies that the students have similar levels of proficiency in this variable.

Particularly, the results suggest that “feeling and sensing” as students’ mathematical problem-solving style is observed. This means that students want to solve math problems within a group and they like to solve math problems and are comfortable trying to learn new things. Moreover, “thinking and intuitive” as students’ mathematical problem-solving style is moderately observed. This indicates that students solve math problems by analyzing all the facts and knowing all the details of the problem.

This finding is supported by a recent study by TIMSS and PISA, which found that students can use their mathematical understanding and knowledge to solve problems (IEA, 2016). PISA assesses students’ ability to use their knowledge and skills in recognizing, analyzing, and solving problems in a variety of situations(OECD, 2019). Moreover, the result confirms the findings of Schoenfeld (2013), who mentioned that the mental state of students is an essential aspect of learning mathematics. The belief system of the student regarding himself, mathematics, and problem-solving determines student progress in problem-solving. This is also agreed by Hendriana et al., (2017) who mentioned that one of the fundamental mathematical abilities that students who study mathematics must develop is the ability to solve problems.

Level of Performance of Students in Solving Math Problems

Table 6 depicts the level of performance of students in solving math problems. The level of performance of students in terms of answering the 40-item Mathematics test has a mean of 60.84 with a descriptive equivalent of above average. This indicates that the performance of students in mathematics is very good.  Furthermore, it has an SD of 10.48. This demonstrates whether one’s scores on mathematical problem-solving style were extremely high or extremely low. This suggests that students’ capacity to solve mathematical problems is more likely to deviate from the mean.

The result shows that the students were able to select and correctly identify the appropriate answer to the given questions. They could choose the correct equation from the problem and accurately determine the answer in the problem scenario. Additionally, they were able to verify their responses by selecting the correct answer to the given problem.

Table 6 Level of Performance of Students in Solving Math Problems

As cited by Heris and Sumarmo (2014), problem-solving style are basic mathematical skills students need to learn. Fernandez et al. (2017) also added that problem-solving is a significant part of learning, making it of particular importance for the study of mathematics. Furthermore, another component of learning mathematics is problem-solving. This means that students need to become proficient in a variety of problem-solving style in mathematics in order to improve their creativity, reasoning, critique, and systematic thinking (NCTM, 2000). Therefore, completing mathematical problems is a crucial component of the learning objectives that must be encountered (Surya et al., 2017).

Significance of the Relationship Between the Variables

Table 7 presents the relationship between Mathematical Problem-Solving Style and the Performance of Students in Mathematics.

The correlation of Mathematical Problem-Solving Style has a significant relationship with the Performance of Students in Mathematics (p<0.05) with a coefficient determination of 0.733. Specifically, there is a strong positive correlation between the variables, and the p-value of the two variables is less than the 0.05 level of significance, which indicates that there is a significant relationship between the mathematical problem-solving style and the performance of students in mathematics (r=0.733,p=0.000. Thus, the null hypothesis is rejected.

Since the result confirmed that the mathematical problem-solving style and performance of students have a very high relationship, this means that the mathematical problem-solving style of the students significantly affects their performance in math. It can be seen from the aforementioned discussion that the mathematical problem-solving style of students is an important factor that affects their performance.

Table 7 Significance of the Relationship Between the Variables

It can be deduced that if students’ mathematical problem-solving styles will be developed, then it enhances their performance in school. This is supported by the study conducted by Suratno et al., (2020) which found that there is a significant positive correlation between students’ problem-solving styles and academic performance of students. Additionally, Mustafić et al. (2017) established that a student would perform exceptionally well in any science subject if they propose a high level of self-concept toward problem-solving skills.

The significant difference in the Mathematical Problem-solving Style of students when grouped according to sex

To determine if there was a significant difference in the mathematical problem-solving style of students when grouped according to sex (male and female), a t-test was used. Table 8 shows the result.

Using the t-test, the obtained t-value is -2.211 and the resulting p-value is 0.9860. This result indicates that the difference in mathematical problem-solving style between males and females is not statistically significant at the 0.05 level. Therefore, the researchers fail to reject the null hypothesis that there is no significant difference in mathematical problem-solving style between male and female students.

Table 8 The significant difference in mathematical-problem solving style of students when grouped according to sex

This means that students’ mathematical problem-solving style when grouped according to sex (male and female) does not vary. This finding is consistent with previous research conducted by Rusdiet al., (2020) who mentioned that men and women have similar mathematical problem-solving styles. Male and female students may understand the information, describe their knowledge, and ask relevant questions. They can use notations, symbols, and mathematical models to describe the problem and solution properly.  Moreover, Goos et al. (2017) studies examined the impact of gender differences on students’ mathematical learning outcomes have yielded inconsistent results. While some studies have demonstrated differences between genders, indicating that either men or women perform better, other studies have found no significant gender differences.

The significant difference in mathematical problem-solving style of students when grouped according to Program

Table 9 presents the significant difference in the mathematical problem-solving style of students when grouped according to four different programs: BTVTED, BSAB, BSOA, and BPA.

The result shows that there is a positive significant difference in the mathematical problem-solving style of students when grouped according to different programs since the f-value is 12.46 and the p-value is 0.000 which is lesser than 0.05 alpha level of significance. It indicates the mathematical problem-solving style of students from BTVTED, BSAB, BSOA, and BPA is significantly different. Therefore, the null hypothesis is rejected.

Table 9 The significant difference in mathematical problem-solving style of students when grouped according to Program.

The result means that students coming from four programs have different problem-solving styles in mathematics. This finding is concurrent to the first model of problem-solving styles based on the concept of psychological functions (Jung, 1923; Moon, 2008; Taylor & Mackenny, 2008). This model consists of four psychological functions as thinking, feeling, sensation, and intuition (Ghodrati et al., 2014). As such, Hacısalihlioğlu et al. (2003)stated that achieving success in problem-solving is linked to possessing abilities such as critical thinking, decision-making, reflective thinking, inquiring, analyzing, and synthesizing. This is further confirmed by Wen-Chun et al. (2015) that students use different problem-solving styles in solving math problems.

SUMMARY, CONCLUSION, AND RECOMMENDATIONS

This chapter presents the summary of the major findings of the study, the conclusion, and the proposed recommendations for possible implementations.

Summary of Findings

The major findings of the study are the following:

• For the level of performance of students in solving math problem, the level of performance of students in terms of answering the 40-item Mathematics test has a mean of 60.84 with a descriptive equivalent of high. This indicates that the performance of students in mathematics is very good. Furthermore, it has an SD of 10.48. This demonstrates whether one’s scores on mathematical problem-solving style were extremely high or extremely low.
• For the level of mathematical problem-solving style, “feeling” got the highest mean of 3.48 with an SD of 0.98. This is followed by “sensing” with a mean of 3.42 and an SD of 0.97. Both indicators got a similar descriptive equivalent of high. On other hand, “intuitive” got the lowest mean of 3.26 and an SD of 1.01 with a descriptive equivalent of moderate. Furthermore, it has an overall mean of 3.38 and an SD of 0.99 with a descriptive equivalent of moderate.
• The statistical analysis shows that there is a strong positive correlation (r=0.733) between the mathematical problem-solving style and students’ performance. The p-value of the two variables is less than 0.05, indicating that the correlation is statistically significant. This means that there is a significant relationship between the students’ mathematical problem-solving style and their academic performance. As a result, the null hypothesis is rejected.
• The statistical analysis shows that there is no significant difference (t=211, p=0.9860) in the mathematical problem-solving style of students when grouped according to sex (male and female). The result indicates that the difference in mathematical problem-solving style between males and females is not statistically significant at the 0.05 level. Therefore, the researchers fail to reject the null hypothesis that there is no significant difference in the mathematical problem-solving style of students when grouped according to sex (male and female). Meanwhile, there is a significant difference (f=12.46, p=0.000) in the mathematical problem-solving style of students when grouped in accordance to different programs since the f-value is 12.46 and the p-value is 0.000 which is lesser than 0.05 alpha level of significance. This indicates the mathematical problem-solving style of students from BTVTED, BSAB, BSOA, and BPA is significantly different. Therefore, the null hypothesis is rejected.
• Based on the results of the study, here are the instructional intervention plan that can be proposed:
• For thinking and intuitive problem solvers – Provide explicit instruction on how to justify mathematical solutions using evidence and logical reasoning. Encourage intuitive problem-solvers to verbalize their thought process and explain how they arrived at their solution. Moreover, provide opportunities for students to develop their critical thinking skills through activities such as puzzles, brain teasers, and logic games. Encourage students to explain their reasoning and thought processes when solving problems.
• For sensing and feeling problem solvers – Provide opportunities for collaborative problem-solvers to work in pairs or small groups to solve problems. Encourage the students to take turns explaining their thought process and to ask questions to deepen their understanding. Provide opportunities for independent problem-solving and self-directed learning while allowing for collaborative work. Furthermore, provide support and guidance as needed to help students build their problem-solving skills. Gradually remove support as students become more confident and independent.

The findings from the study led the researcher to draw the following conclusions:

• Performance of students in solving math problems is very good.
• Mathematical problem-solving style of students is moderately observed.
• There is a significant relationship between mathematical problem-solving style and the performance of students.
• There is no significant difference in the mathematical problem-solving style of students when grouped according to sex. However, students’ mathematical problem-solving style is significantly different when grouped according to different programs.
• Understanding students’ different mathematical problem-solving styles can help instructors tailor their instruction to meet the needs of different learners and create a more inclusive classroom environment.

Recommendations

Based on the findings, analysis, and conclusion drawn in this study, the following recommendations were summarized:

• Students are encouraged to learn effectively and independently in solving mathematical tasks. They may discover that strengthening their different mathematical problem-solving style would boost their performance in math. This can be achieved by helping them discover their different problem-solving styles and identifying strategies that can assist them in solving mathematical problems. By doing so, they can improve their performance in mathematics and unleash their full potential in this subject area.
• Instructors, college administrators, and local college officials are urged to develop enrichment activities to help their students in developing their mathematical problem-solving styles. This is especially true when it comes to encouraging students to take the initiative, establish their own mathematical problem-solving strategy, set learning goals, and assess their abilities to specify the sources they need to learn, particularly in mathematics. Teachers may create engaging instructional intervention programs to deepen students’ interests in problem-solving in Mathematics. Furthermore, they can engage in and be creative with technology, mentoring, and coaching students who are having difficulty completing mathematical problems.
• The current study’s findings emphasize the relevance of problem-solving style in mathematics and provide ways to apply and improve it. The establishment of a curriculum and instructional strategy for applying it to students is required so that teachers and students can continue and maximize their mathematical problem-solving style. To ensure that these tactics are embedded in students, ongoing efforts will be required. To maximize students’ problem-solving style in Mathematics, STCAST instructors and administrators should work collaboratively to achieve the objective. They should ensure that the necessary materials, resources, activities, and differentiated instruction are available and used to meet students’ needs to be motivated in learning.
• Future research for developing other intervention programs needed to identify the factors that might improve the mathematical problem-solving style to enhance students’ performance.

  REFERENCES

• Ahmed, W., van der Werf, G., Kuyper, H., & Minnaert, A. (2013). Emotions, Self-Regulated Learning, and Achievement in Mathematics: A Growth Curve Analysis. Journal of Educational Psychology, 105, 150-161.https://doi.org/10.1037/a0030160
• Ali, Norhidayah, Jusoff, Kamaruzaman, Ali, Syukriah, Mokhtar, Najah and Salamt, Azni Syafena Andin. (20 December 2009). ‘The Factors Influencing Students’ Performance at Universiti Teknologi MARA Kedah, Malaysia’. Canadian Research & Development Center of Sciences and Cultures: Vol.3 No.4.
• Altun, İ. (2003). The perceived problem-solving ability and values of student nurses and midwives. Nurse education today, 23(8), 575-584.
• Almagro, R.E., Montepio, H.C. & Tuquib, M.O. (2023). E-Learning Educational Atmosphere and Technology Integration as Predictors of Students’ Engagement: The Case of Agribusiness Program. Industry and Academic Research Review, 4 (1), 339-343.
• Almagro, R. E., EDIG, M. M.  (2023). Motivated Strategies for Learning Mathematics as Influenced by Computer Attitude and Social Media Engagement of Students. Journal of Social, Humanity, and Education.
• Amran, M. S., Bakar, A. Y. A. (2020). We Feel, Therefore We Memorize: Understanding Emotions in Learning Mathematics Using Neuroscience Research Perspectives. Universal Journal of Educational Research, 8(11B),5943-5950. DOI: 10.13189/ujer.2020.082229.
• Badger, M. S., Sangwin, C. J., Hawkes, T. O., Burn, R. P., Mason, J., & Pope, S. (2012). Teaching Problem-Solving in Undergraduate Mathematics. Coventry, UK: Coventry University https://doi.org/10.1017/CBO9781107415324.004.
• Blackwell, L.S., Trzesniewski, K.H., & Dweck, C.S.(2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78 (1), 246– 263. https://doi.10.1111/j.1467-8624.2007.00995.x
• Bradley, M. (2012). Problem-solving differences between men and women. Retrieved from http://www.healthguidance.org/entry/13967/1/ProblemSolving–Differences-Between-Men-and-Women.html
• Bishop, J. P. (2012). “She’s always been the smart one. I’ve always been the dumb one”: Identities in the mathematics classroom. Journal for Research in Mathematics Education, 43(1), 34. https://doi:10.5951/jresematheduc.43.1.0034
• Burkey, L. A., & Miller, M. K. (2005). Examining gender differences in intuitive decision making in the workplace. Gender and Behavior, 3, 252-268.
• Cribbs, J. D., Hazari, Z., Sonnert, G., & Sadler, P. M.(2015). Establishing an Explanatory Model for Mathematics Identity. Child Development, 86 (4),1048– 1062. https://doi.org/10.1111/cdev.12363
• Conner, M. G. (2000). Understanding the difference between men and women. Family Relations, 32, 567-573.IEA. (2016). The TIMSS 2015 International Results in Mathematics. In TIMSS & PIRLS International Study Center. Retrieved from http://timss2015.org/.
• Fernandez, M.L, Hadaway, N., & Wilson, J.W. (2017). Mathematical Problem Solving. http://jwilson.coe.uga.edu/emt725/PSsyn/PSsyn.html
• Foshay, R., & Kirkley, J. (2003). Principles for teaching problem solving. Plato Learning, 1–16. https://doi.org/10.1.1.117.8503&rep=rep1&type=pdf
• GANZON, W. J., & EDIG, M. M. . (2022). Time Management And Self-Directed Learning As Predictors Of Academic Performance Of Students In Mathematics. Journal of Social, Humanity, and Education, 3(1), 57–75. https://doi.org/10.35912/jshe.v3i1.1212
• Glass, G. V & Hopkins, K.D. (1984). Statistical Methods in Education and Psychology, 2nd Edition. Englewood Cliffs, NJ: Prentice-Hall.
• Ghodrati, M., Bavandian, L., Moghaddam, M. M., & Attaran, A. (2014). On the relationship between problem-solving trait and the performance on Ctest. Theory and Practice in Language Studies, 4(5), 1093-1100.
• Gürsan, S., & Yazgan, Y. (2020). Non-Routine problem-solving skills of ninth grade students: An experimental study. Academy Journal of Educational Sciences, 4(1), 23-29.
• Hafriani. (2018). Karakteristik Intuisi Mahasiswa UIN dalam Memecahkan Masalah Matematika Ditinjau Dari Kemampuan Berpikirnya. Pedagogik, Volume 1, Nomor 2
• Heris and Sumarmo. (2014). Penilaian Pembelajaran Matematika. P T Refika Aditama
• Hendriana, H., Rohaeti, E. E., &Sumarmo, U. (2017). Hard skills and soft skills mathematical. Bandung: Refika Aditama.
• Hsieh, Y.-P., & Lin, C.-H. (2006). Mathematical problem-solving style as a function of sensory preference. Educational Studies in Mathematics, 63(3), 321-335.
• Jung, J. Y., Youn, H. O., & Kim, H. J. (2007). Positive thinking and life satisfaction amongst Koreans. Yonsei Medical Journal, 48(3), 371-378.
• Khan, M. J., Younas, T., & Ashraf, S. (2016). Problem Solving Styles as Predictor of Life Satisfaction Among University Students. Pakistan Journal of Psychological Research, 31(1).
• Kneeland, S. (2001). Problem Çözme (N. Kalaycı, Çev.). Ankara: Gazi Kitabevi. Konan, N.(2013). Relationship between locus of control and problem-solving skills of high school administrators. International Journal of Social Sciences and Education, 3(3), 786-794.
• Macaso, K. M. J., & Dagohoy, R. G. (2022). Predictors of Performance in Mathematics of Science, Technology And Engineering Students of a Public Secondary School in The Philippines. Journal of Social, Humanity, and Education, 2(4), 311-326.
• Moon, J. (2008). Critical thinking: An exploration of theory and practice. New York: Routledge.
• Mustafić, M., Niepel,  C.,  &  Greiff,  S.  (2017). Assimilation and contrast effects in the formation of problem-solving self-concept. Learning  and  Individual  Differences, 54, 82-91
• NCTM. (2000). Principles and Standards for School Mathematics. United States of America: NCTM.
• OECD. (2019). PISA 2018 Results: What Student Know and Can Do. https://doi.org/10.1787/5f07c754-en.
• Plaks, J. E., & Stecher, K. (2007). Unexpected improvement, decline, and stasis: A prediction confidence perspective on achievement success and failure. Journal of Personality and Social Psychology,93 (4), 667– 684. https://doi.org/10.1037/0022-3514.93.4.667
• Polya, G. (1957). How To Solve It: A New Aspect of Mathematical Method (Second). https://doi.org/10.2307/j.ctvc773pk.
• Rusdi, M., Fitaloka, O., Basuki, F. R., & Anwar, K. (2020). Mathematical Communication Skills Based on Cognitive Styles and Gender. International Journal of Evaluation and Research in Education, 9(4), 847-856.
• Saeed, R. (2015). Use of smartphones and social media in medical education: trends, advantages, challenges and barriers. Acta informatica medica, 27(2), 133.
• Surya, E., & Putri, F. A. (2017). Improving Mathematical Problem-Solving Ability and Self-Confidence of High School Students through Contextual Learning Model. Journal on Mathematics Education, 8(1), 85-94. http://dx.doi.org/10.22342/jme.8.1.3324.85-94
• Sutherland, L. (2002). Developing problem-solving expertise: the impact of instruction in a question analysis strategy. Learning and Instruction, 12(2), 155-187.
• Tan, L. (2014). Correlational study: W. F. Thompson Music in the social and behavioral sciences. Thousand Oaks: SAGE Publications, 1(3), 269-271.
• Taylor, G. R., & Mackenny, L. (2008). Improving human learning in the classroom: Theories and teaching practices. New York: Rowman & Littlefield Education
• Vicente, R. S., Flores, L. C., Almagro, R. E., Amora, M. R. V., & Lopez, J. P. (2023). The Best Practices of Financial Management in Education: A Systematic Literature Review. International Journal of Research and Innovation in Social Science, 7(8), 387-400.DOI: https://dx.doi.org/10.47772/IJRISS.2023.7827
• Wahono, B., Chang, C. Y., & Retnowati, A. (2020). Exploring a direct relationship between students’ problem-solving abilities and academic achievement: A STEM education at a coffee plantation area. Journal of Turkish Science Education, 17(2), 211-224.
• Wijayanti, A., Herman, T., & Usdiyana, D. (2017). The implementation of CORE model to improve students’ mathematical problem-solving ability in secondary school. Advances in Social Science, Education and Humanities Research, 57, 89–93. https://doi.org/10.2991/icmsed-16.2017.20.

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The Effect of Learning Strategy and Cognitive Style toward Mathematical Problem Solving Learning Outcomes

This study aims to analyze and determine the effect of learning strategies (The differences between Problem Based Learning and Direct Instruction) and cognitive style on mathematical problems solving learning outcomes. This study used a quasi-experimental research design (nonequivalent control group designs) The research subjects were students from state elementary school of V grade in Gading I Surabaya, east java. The totals of elementary school population in east Surabaya were 141 elementary schools. The selected schools are the schools which have some parallel classes, therefore the circumstances are relatively similar. Four classes are selected, they were from SDN Gading 1 state elementary school. These schools were randomly chosen to implement different learning strategies, they are implementing problem-based learning and hands-on learning. The data collection techniques were mathematical problems solving learning pre-tests and post-test, Group embedded figures test (GEFT) for cognitive style, assessment performance for learning achievement. Data analysis techniques in this study use ANOVA technique (analysis of variance) two lines. The results of this study showed that (1) there is a difference in learning achievement between the students' groups who are taught by PBL and direct learning; (2) there are differences in achieving learning outcomes toward students' group and different cognitive styles; (3) there are significant interactions between the use of learning strategies and cognitive style on learning outcomes.

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