Wellcome Open Research

We need an interdisciplinary approach to solve real world problems: a case study from the covid-19 pandemic.

interdisciplinary approach

As a research community, we need to change our ways of working to solve real world problems in real time. An interdisciplinary approach is urgently needed, bringing together experts and knowledge from across the full spectrum of research disciplines.

In this blog, Trisha Greenhalgh, Mustafa Ozbilgin, and Damien Contandriopoulos consider how a lack of interdisciplinarity impacted the public health discourse and policy around the transmission of COVID-19. Keep reading to learn more about their fascinating Research Article on Wellcome Open Research.

COVID-19 is most certainly an airborne virus, but policies for managing its spread remain focused on handwashing, and place little emphasis on airborne precautions. This ‘droplet dogma’ has prevailed since the beginning of the pandemic, despite the clear (and ever-growing) evidence for airborne transmission.

But how did we get here? How have public health discourse and policy failed to properly consider airborne transmission? Why does droplet science continue to hold its position in the mainstream?

Power and knowledge

The concepts of orthodoxy and heterodoxy are central in answering these questions.

Every field of research has its own set of orthodoxies (beliefs which are established and considered legitimate) and heterodoxies (marginal, fringe beliefs which are dismissed and not widely accepted yet, legitimate only in another field of science), but the COVID-19 pandemic brings the stand-off between these positions into the spotlight.

Our article on Wellcome Open Research draws on the work of French sociologist Pierre Bourdieu to look at how knowledge and power played out between orthodox and heterodox groups of scientists throughout the pandemic.

Orthodoxy and heterodoxy in the COVID-19 pandemic

Even before the coronavirus outbreak, two groups of scientists in different fields held very different views on the transmission of respiratory viruses.

The accepted, orthodox position is held by infectious disease researchers and IPC (Infection Prevention and Control) scientists, including doctors and nurses based in hospitals. This group traditionally research diseases for which handwashing is a key preventative measure.

Conversely, the heterodox position consists of aerosol scientists who study the flow of airborne particles – including engineers, chemists, architects, and others interested in the physical environment and how things move through it.

Since the beginning of the COVID-19 pandemic, researchers in the heterodox position have found it difficult to challenge the orthodoxy because they lacked the power needed to successfully assert that the virus is airborne against the accepted droplet discourse.

How did the ‘droplet dogma’ begin?

It’s interesting to consider a case study from the World Health Organization when asking how the orthodox position became so entrenched, particularly in the West.

At the WHO’s first international press conference on COVID-19 back in February 2020, Director-General Tedros Adhanom declared “corona[virus 19] is airborne”. He then immediately corrected himself: “Sorry, I used the military word, airborne. It meant to spread via droplets or respiratory transmission. Please take it that way; not the military language.” A little over a month later, the WHO confirmed on Twitter that “COVID is not airborne”, and the recommendations and public health measures that followed were all based on droplet transmission.

When comparing this case study with Japan, where the possibility of aerosol transmission of COVID-19 was accepted from the outset, the difference is clear.

Japan’s ‘three Cs’ campaign advised the public to avoid closed spaces, crowded places, and close-contact settings. Inter-field struggles between orthodox and heterodox positions were not so marked in Japan, allowing their local policymakers to embrace a wider range of hypotheses and research methods.

The solution: an interdisciplinary approach

What is interdisciplinarity.

Ironically, interdisciplinarity is defined differently by different disciplines. Some definitions focus on interdisciplinarity as collaboration , where a combination of different skills and knowledge come together to address a complex research challenge.

For the purpose of our research, we have followed Rowland and defined it as contestation . This means that although interdisciplinary approaches can bring inevitable conflict, the outcomes of these clashes could be positive – for example, generating new insights or knowledge.

What does an interdisciplinary approach look like?

An interdisciplinary approach to any real world issue, including the COVID-19 pandemic, needs to incorporate two key areas:

  • Inclusive work practices
  • Radical changes to governance

We recommend the adoption of what Nowotny et al call ‘Mode 2 knowledge production’ – an approach to science which is:

  • Socially distributed
  • Application-oriented
  • Inherently transdisciplinary
  • Involves a wide range of stakeholders, including researchers and the lay public

Acknowledging the evidence on airborne transmission opens up a range of possibilities, including:

  • Creation of higher-grade, better-fitting masks
  • Improvement of building safety through ventilation and air filtration
  • Support for work-from-home policies, to reduce crowding in shared workspaces

During the last 22 pandemic months, science has sometimes progressed at breakneck speed. New discoveries such as vaccines were rapidly implemented and scaled up. But in other areas such as preventive public health, policy has simply not kept pace with the latest research. Our paper puts forward a political and sociological explanation for this entrenchment.

We hope that better understanding of why aerosol science is being ignored, and a move towards interdisciplinary ways of working, may help break droplet orthodoxy’s current grip on infection control policy.

You can read the full Research Article and the peer review reports via Wellcome Open Research, ‘Orthodoxy, illusio, and playing the scientific game: a Bourdieusian analysis of infection control science in the COVID-19 pandemic [version 2; peer review: 2 approved]’ >>

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October 1, 2018

To Solve Real-World Problems, We Need Interdisciplinary Science

Solving today’s complex, global problems will take interdisciplinary science

By Graham A. J. Worthy & Cherie L. Yestrebsky

interdisciplinary problem solving

T he Indian River Lagoon, a shallow estuary that stretches for 156 miles along Florida's eastern coast, is suffering from the activities of human society. Poor water quality and toxic algal blooms have resulted in fish kills, manatee and dolphin die-offs and takeovers by invasive species. But the humans who live here have needs, too: the eastern side of the lagoon is buffered by a stretch of barrier islands that are critical to Florida's economy, tourism and agriculture, as well as for launching NASA missions into space.

As in Florida, many of the world's coastlines are in serious trouble as a result of population growth and the pollution it produces. Moreover, the effects of climate change are accelerating both environmental and economic decline. Given what is at risk, scientists like us—a biologist and a chemist at the University of Central Florida—feel an urgent need to do research that can inform policy that will increase the resiliency and sustainability of coastal communities. How can our research best help balance environmental and social needs within the confines of our political and economic systems? This is the level of complexity that scientists must enter into instead of shying away from.

Although new technologies will surely play a role in tackling issues such as climate change, rising seas and coastal flooding, we cannot rely on innovation alone. Technology generally does not take into consideration the complex interactions between people and the environment. That is why coming up with solutions will require scientists to engage in an interdisciplinary team approach—something that is common in the business world but is relatively rare in universities.

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Universities are a tremendous source of intellectual power, of course. But students and faculty are typically organized within departments, or academic silos. Scientists are trained in the tools and language of their respective disciplines and learn to communicate their findings to one another using specific jargon.

When the goal of research is a fundamental understanding of a physical or biological system within a niche community, this setup makes a lot of sense. But when the problem the research is trying to solve extends beyond a closed system and includes its effects on society, silos create a variety of barriers. They can limit creativity, flexibility and nimbleness and actually discourage scientists from working across disciplines. As professors, we tend to train our students in our own image, inadvertently producing specialists who have difficulty communicating with the scientist in the next building—let alone with the broader public. This makes research silos ineffective at responding to developing issues in policy and planning, such as how coastal communities and ecosystems worldwide will adapt to rising seas.

Science for the Bigger Picture

As scientists who live and work in Florida, we realized that we needed to play a bigger role in helping our state—and country—make evidence-based choices when it comes to vulnerable coastlines. We wanted to make a more comprehensive assessment of both natural and human-related impacts to the health, restoration and sustainability of our coastal systems and to conduct long-term, integrated research.

At first, we focused on expanding research capacity in our biology, chemistry and engineering programs because each already had a strong coastal research presence. Then, our university announced a Faculty Cluster Initiative, with a goal of developing interdisciplinary academic teams focused on solving tomorrow's most challenging societal problems. While putting together our proposal, we discovered that there were already 35 faculty members on the Orlando campus who studied coastal issues. They belonged to 12 departments in seven colleges, and many of them had never even met. It became clear that simply working on the same campus was insufficient for collaboration.

So we set out to build a team of people from a wide mix of backgrounds who would work in close proximity to one another on a daily basis. These core members would also serve as a link to the disciplinary strengths of their tenure home departments. Initially, finding experts who truly wanted to embrace the team aspect was more difficult than we thought. Although the notion of interdisciplinary research is not new, it has not always been encouraged in academia. Some faculty who go in that direction still worry about whether it will threaten their recognition when applying for grants, seeking promotions or submitting papers to high-impact journals. We are not suggesting that traditional academic departments should be disbanded. On the contrary, they give the required depth to the research, whereas the interdisciplinary team gives breadth to the overall effort.

Our cluster proposal was a success, and in 2019 the National Center for Integrated Coastal Research (UCF Coastal) was born. Our goal is to guide more effective economic development, environmental stewardship, hazard-mitigation planning and public policy for coastal communities. To better integrate science with societal needs, we have brought together biologists, chemists, engineers and biomedical researchers with anthropologists, sociologists, political scientists, planners, emergency managers and economists. It seems that the most creative perspectives on old problems have arisen when people with different training and life experiences are talking through issues over cups of coffee. After all, “interdisciplinary” must mean more than just different flavors of STEM. In academia, tackling the effects of climate change demands more rigorous inclusion of the social sciences—an area that has been frequently overlooked.

The National Science Foundation, as well as other groups, has recently required that all research proposals incorporate a social sciences component, as an attempt to assess the broader implications of projects. Unfortunately, in many cases, simply adding a social scientist to a proposal is done only to check a box rather than to make a true commitment to allowing the discipline to inform a project. Instead social, economic and policy needs must be considered at the outset of research design, not as an afterthought. Otherwise our work might fail at the implementation stage, which means we are not being as effective as we could be in solving real-world problems. As a result, the public might become skeptical of how much scientists can contribute toward solutions.

Connecting with the Public

The reality is that communicating research findings to the public is an increasingly critical responsibility of scientists. Doing so has a measurable effect on how politicians prioritize policy, funding and regulations. UCF Coastal was brought into a world where science is not always respected—sometimes it is even portrayed as the enemy. There has been a significant erosion of trust in science over recent years, and we must work more deliberately to regain it. The public, we have found, wants to see quality academic research that is grounded in the societal challenges we are facing. That is why we are melding pure academic research with applied research to focus on issues that are immediate—helping a town or business recovering from Hurricane Irma, for example—as well as long term, such as directly advising a community how to build resiliency as flooding becomes more frequent.

As scientists, we cannot expect to explain the implications of our research to the wider public if we cannot first understand one another. A benefit of regularly working side by side is that we are crafting a common language, reconciling the radically different meanings that the same words can have to a variety of specialists. Finally, we are learning to speak to one another with more clarity and understand more explicitly how our niches fit into the bigger picture. We are also more aware of culture and industry as driving forces in shaping consensus and policy. Rather than handing city planners a stack of research papers and walking away, UCF Coastal sees itself as a collaborator that listens instead of just lecturing.

This style of academic mission is not only relevant to issues around climate change. It relates to every aspect of modern society, including genetic engineering, automation, artificial intelligence, and so on. The launch of UCF Coastal has garnered positive attention from industry, government agencies, local communities and academics. We think that is because people do want to come together to solve problems, but they need a better mechanism for doing so. We hope to be that conduit while inspiring other academic institutions to do the same.

After all, we have heard for years to “think globally, act locally,” and that “all politics is local.” Florida's Indian River Lagoon will be restored only if there is engagement among residents, local industries, academics, government agencies and nonprofit organizations. As scientists, it is our responsibility to help everyone involved understand that problems that took decades to create will take decades to fix. We need to present the most helpful solutions while explaining the intricacies of the trade-offs for each one. Doing so is only possible if we see ourselves as part of an interdisciplinary, whole-community approach. By listening and responding to fears and concerns, we can make a stronger case for why scientifically driven decisions will be more effective in the long run.

Scientific American Magazine Vol 319 Issue 4

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  • Published: 03 August 2020

A practical guideline how to tackle interdisciplinarity—A synthesis from a post-graduate group project

  • Max Oke Kluger   ORCID: orcid.org/0000-0001-9130-8948 1 &
  • Gerhard Bartzke 1  

Humanities and Social Sciences Communications volume  7 , Article number:  47 ( 2020 ) Cite this article

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  • Environmental studies

The comprehensive understanding of increasingly complex global challenges, such as climate change induced sea level rise demands for interdisciplinary research groups. As a result, there is an increasing interest of funding bodies to support interdisciplinary research initiatives. Attempts for interdisciplinary research in such programs often end in research between closely linked disciplines. This is often due to a lack of understanding about how to work interdisciplinarily as a group. Useful practical guidelines have been provided to overcome existing barriers during interdisciplinary integration. Working as an interdisciplinary research group becomes particularly challenging at the doctoral student level. This study reports findings of an interdisciplinary group project in which a group of doctoral students and postdoctoral researchers from various disciplines faced the challenges of reconciling natural, social, and legal aspects of a fictional coastal environmental problem. The research group went through three phases of interdisciplinary integration: (1) comparing disciplines, (2) understanding disciplines, and (3) thinking between disciplines. These phases finally resulted in the development of a practical guideline, including five concepts of interactive integration. A reflective analysis with observations made in existing literature about interdisciplinary integration further supported the feasibility of the practical guideline. It is intended that this practical guideline may help others to leave out pitfalls and to gain a more successful application of interdisciplinarity in their training.

Introduction

The large economic, ecological, and demographical challenges caused by globalization led to the transition towards interdisciplinary collaborations between scientific communities, policymakers, and society (Langfeldt et al., 2012 ; Pedersen, 2016 ). Integration of diverse understandings by interdisciplinary collaboration is seen as most comprehensive approach to complex environmental problems (Bromham et al., 2016 ; Ledford, 2015a ; Wagner et al., 2011 ). For example, a paragon for addressing a complex environmental problem was reported for Nova Scotia, Eastern Canada. In this study a group of decision makers from industry, policy, research, communities, as well as, fishery assessed an interdisciplinary way to sustainably harness tidal energy potential (Palmer, 2018 ). In academia, however, discoveries are said to be more likely on the boundaries between disciplines. In this case the latest methods and perspectives can increase knowledge during interdisciplinary research collaborations (Rylance, 2015 ). In contrast, single-disciplinary and multi-disciplinary research collaborations increase impact output in highly specialized fields. Therefore, interdisciplinary research collaboration fosters deeper interaction and integration of various disciplinary perspectives (Bergen et al., 2020 ; Gewin, 2014 ; Pykett et al., 2020 ; Sá, 2008 ; Van Noorden, 2015 ).

In order to successfully investigate intricate problems, all involved parties have to communicate and collaborate in an attempt to create a common understanding and to learn from each other’s perspectives. This ideally results in a new perspective that is more than the sum of its components (Brewer, 1999 ; Nissani, 1997 ; Tauginienė et al., 2020 ). As a result, global governance recognizes interdisciplinary research as the best way to address emerging multifaceted problems. Therefore, interdisciplinary programs were strongly encouraged over the last decades (Bozeman and Boardman, 2014 ; Ledford, 2015b ; Pedersen, 2016 ; Rylance, 2015 ), including interdisciplinary research graduate programs. Among others, the US graduate program Integrative Graduate Education and Research Traineeship Footnote 1 (IGERT) and the Toolbox Dialogue Initiative Footnote 2 appear to be good showcases for interdisciplinary approaches (Eigenbrode et al., 2007 ; Goring et al., 2014 ; Kennedy et al., 2012 ; Laursen, 2018 ; Pennington et al., 2013 ; Steel et al., 2017 ). Another typical example for an interdisciplinary research training program is the Trust and Communication in a Digitized World program, which examines how trust can be developed and maintained under the conditions of new forms of communication. Footnote 3

To date, a broad range of interdisciplinary graduate education programs have been established to address cross-cutting environmental and sustainability problems (Bruce et al., 2004 ; Campbell, 2005 ; Graybill et al., 2006 ; Juhl et al., 1997 ; McCarthy, 2004 ; Morse et al., 2007 ; Morss et al., 2005 ; Rhoten and Parker, 2004 ; Skates, 2003 ). Nonetheless, from the doctoral student’s perspective the focus on interdisciplinary research may not be trivial, because in order to conclude their work in a time frame that is often narrowly predetermined, doctoral students rarely have the opportunity to gain a deeper understanding of disciplines outside of their own field (Welch-Devine, 2012 ; Welch-Devine and Campbell, 2010 ). Collaboration efforts mostly come in the form of the exchange of expertise between closely related disciplines, for example in collaborations between geology and biology. In such disciplinary and cross-disciplinary investigations the integration of disciplines is straightforward. However, interdisciplinary collaboration efforts between disciplines not as obviously related to each other, for example social and natural sciences, can introduce misunderstandings because of stereotypes (MacLeod, 2018 ). This can hinder research progress, leads to unnecessary repetition or, in the worst case, can have negative consequences when misunderstood theories are applied in improper contexts (Campbell, 2005 ). In post-graduate training programs, these problems are further complicated as doctoral students are still at the stage of mastering the vocabulary of their own disciplines, while, because of the large time effort, being less interested in working out the meaning from another discipline’s perspective.

Practical guidelines from established literature are commonly the first choice to tackle interdisciplinary integration and research process (Brandt et al., 2013 ; Brown et al., 2015 ; Lang et al., 2012 ). It is also beneficial to reflect on assumptions originating from the different disciplinary perspectives. An efficient communication framework favours respectful attitudes within the research group, resulting in effective cooperation rather than competition. Repko and Szostak ( 2020 ) and Menken and Keestra ( 2016 ) synthesized case studies about interdisciplinarity and provided a good roadmap and interdisciplinary research model how to work interdisciplinarily.

One of the most prominent examples for interdisciplinarity is the effect of climate change on the coastal environment. It comprises of an interacting web of various disciplines covering, for example, atmospheric and oceanographic issues, biological consequences, economic interests, societal concerns, legal commitments, political action as well as ethical implications. Our study aims to extent the existing scientific literature about interdisciplinary integration by focusing on the perspective of post-graduates working in the coastal environment. We reflect on an interdisciplinary group project in which doctoral students and postdoctoral researchers from the interdisciplinary training program INTERCOAST, having different single disciplinary backgrounds, faced challenges of interdisciplinarity in a fictional coastal environmental problem. From our observations about advantages and challenges of interdisciplinarity, a practical guideline was synthesized that could help to educate post-graduates with different backgrounds to face an interdisciplinary problem as a group and how to bypass the pitfalls when it comes to interdisciplinary group work.

Background and composition of the group project

The post-graduate training group Integrated Coastal Zone and Shelf-Sea Research (INTERCOAST) was funded by the Deutsche Forschungsgemeinschaft from 2009 to 2018 and was a collaboration between the University of Bremen (Germany) and the University of Waikato (New Zealand). The premier goal of INTERCOAST was to gain an integrated understanding of the coastal environment from oceanographic, sedimentological, biological, socio-economic, and legal perspectives. INTERCOAST consisted of 47 individual research projects, which until now resulted in ca. 100 publications in peer-reviewed journals and books. At present, the majority of these publications aimed on disciplinary research questions, whereas only few interdisciplinary studies have been published (Koschinsky et al., 2018 ; Markus et al., 2015 ). Apart from the high level of disciplinary research, the focus of INTERCOAST was also set on interdisciplinary education, which was provided to the post-graduates through workshops and group projects.

From October 2014 to September 2015, 12 doctoral students and two postdoctoral researchers set up an interdisciplinary group project in which a problem related to the coastal environment was investigated to gain a better understanding from different disciplines. The proponents involved in the group project came from different academic disciplines and therefore had considerably different professional expertise about the coastal environment. Research topics that were covered by the proponents of the group project included, but were not limited to, studying iron enrichment in coastal sand deposits (Kulgemeyer et al., 2017 ), various coastal erosion processes (Bartzke et al., 2018 ; Biondo and Bartholomae, 2017 ; Blossier et al., 2017 ; Kluger et al., 2017 , 2019 ; Staudt et al., 2017 ), expansion mechanisms of invasive seaweeds (Bollen et al., 2017 ), the public discourse of coastal protection in Germany (Scheve, 2017 ), and legislative differences between Germany and New Zealand regarding underwater cultural heritage. The proponents of the group consisted of geoscientists, biologists, social scientists, and legal scientists, with geoscientists representing the majority (Table 1 ). The bias in group composition arose from the large quantity of individual research projects that focused on geoscientific topics. The number of group proponents was restricted to 14 as this was the number of doctoral students and postdoctoral researchers who were available during the time period of the group project.

Setup of the group project

Literature reports that three main goals of interdisciplinary and transdisciplinary research efforts need to be established within their own programmatic routines (Brandt et al., 2013 ; Lang et al., 2012 ). First, a research group forms around a commonly agreed integrated research question. To this end, it is important to identify an aim that does not privilege any involved discipline over another (Campbell, 2005 ). Further, it is necessary to create a common understanding of the different disciplinary concepts, vocabulary, methods, and values. Finally, an interactive communication framework needs to be set up to allow for an efficient sharing of the on-going research within the group. The group project reported in this study lasted for 11 months and was divided into three phases (Table 2 ), which were loosely associated with the three goals of interdisciplinary and transdisciplinary research described above: (1) phrasing an integrated research question, (2) creating a common understanding, and (3) establishing an interactive communication framework.

During the first 9 months (Phase 1), the postdoctoral researchers organized monthly group meetings. These group meetings consisted of an informal joint lunch break and a subsequent formal seminar. The formal seminar commonly lasted for 2 h and was organized and moderated by the postdoctoral researchers. In the first formal seminar, the group brainstormed about interdisciplinary topics related to the coastal environment in an open discussion. The decision about whether a topic was considered interesting and relevant to the coastal environment was made based on a rather superficial discussion among the group, without taking external expertise or research into account. The selection of relevant topics was not based on democratic decision, for example by means of a vote. A topic was considered interesting and relevant to the coastal environment when at least one proponent of the group supported the suggested topic and nobody expressed an objection. From these topics, the group chose the most interesting and relevant topic and framed a common research problem for further literature research. Wind energy production was selected as common research problem due to its broad applicability to the different disciplines and its prominence in the context of current societal and technical developments related to climate change. The agreement about a common research problem was achieved by an open vote.

The next step consisted of literature research: Each post-graduate had the task to familiarize themselves with one aspect of the common research problem, e.g. noise emission of wind turbines, while focusing on differences between the four disciplines, and prepared a short 10-min presentation about the selected aspect of the common research problem. The group did not monitor how long each individual post-graduate worked on the literature research and the preparation of the presentation. During seminars 2–6, the post-graduates presented their selected aspect of the common research problem to the entire group. Each presentation was followed by a 30-min to 1-h discussion phase during which the proponents of the group discussed the presented aspect in the light of their personal knowledge.

During seminars 7–9, the group phrased a commonly agreed research question. This process started with a discussion about the expected final outcome of the group project. At the end of the seventh seminar, the group agreed on (1) framing one integrated research question related to the common research problem and (2) answering this question interdisciplinarily. The eighth seminar was spent on framing the integrated research question. Several research questions were suggested by proponents of the group. Out of the several research questions, the group established a commonly agreed interdisciplinary research question by means of an open vote, namely:

“How do natural, social, and legal disciplines change in importance and interconnectivity when comparing potential wind farm locations (a) offshore within exclusive economic zone, (b) offshore within territorial sea, and (c) onshore near the coast?”

The ninth seminar was spent by the group to discuss and agree on the strategy to answer the integrated research question. The group decided to answer the integrated research question through phases 2 and 3 as explained below. The group did not monitor the involvement of individual group proponents during the process of phrasing the integrated research question. The authors therefore cannot judge about whether the idea of studying an interdisciplinary problem with a common research question was triggered by a single proponent of the group, or rather developed successively from the entire group’s discussion.

During the 10th month (Phase 2), the proponents of the group were asked to split into four multidisciplinary subgroups and prepared 30-min group presentations, which were supposed to address the research question by focusing on one of the four disciplines (Table 1 ). Each subgroup consisted of one expert from her/his own discipline by training. The other proponents of the subgroups had professional expertise in one of the four disciplines. For example, a social scientist, two geoscientists, and one biologist formed a subgroup with focus on societal aspects in respect to the research question. The social scientist was the expert of this subgroup, moderated the progress within the subgroup, and could help the other proponents of the subgroup in case of misunderstandings related to social scientific issues. The subgroups formed randomly; because of the relatively small number of proponents participating in the group project, they were not always composed of researchers from all four disciplines. The content of the group presentation was divided equally among the proponents of the subgroup to provide the possibility that every proponent would contribute equally to the outcome of their group presentation. The group did not monitor how long each subgroup prepared themselves for their group presentation. The authors acknowledge that an equal contribution is difficult to judge upon due to different personalities of the group proponents. One proponent might spend more effort and time to her/his part of the group presentation than others, or vice versa. The subgroups presented their findings to the entire group during the first day of a 2-day off-campus retreat. Each of the four presentations were followed by a discussion phase. During the discussion phase, the proponents of the group were asked to focus on how the four disciplines addressed the research question in their group presentation. This approach was chosen to create a common understanding of the different disciplinary concepts, vocabulary, methods, and values relevant to the research question. The final outcome of the discussion phase was the common agreement throughout the group to perform a role play as interdisciplinary interactivity.

Phase 3 started with the role play, which was conducted on the second day of the 2-day off-campus retreat. The aim of the role play was to transfer the integrated knowledge gained from the group presentations into an interactive communication framework. The role play included a 2-h planning phase, followed by a 2-h preparation phase, the actual role play (ca. 1 h), and was completed with a 2-h discussion phase. In the planning phase, the proponents of the group nominated different communication scenarios in which the research question could be addressed by all four disciplines. The group decided that the role play would be framed in an open forum in which actors, representing the four disciplines’ interests, would discuss where to construct a future fictional wind farm in Germany. Afterwards, all group proponents slipped into a role and prepared themselves for their part in the role play. One group proponent proposed the role of a moderator. The proponents chose roles based on their individual interests and preferences in order to increase motivation and to maintain a long and interesting discussion among proponents of the group.

The role play took place in a seminar room and the actors were seated in a circle of chairs. The moderator started the role play by introducing him/herself and the reasoning for an open forum. Subsequently, the other actors introduced their role and made a first statement in which they highlighted their role and their role’s opinion, as in the case of the present study, in the process of wind farm construction. Afterwards, a discussion started among the actors. This discussion was only loosely framed by the moderator, giving the actors space to freely interact and communicate within the group and respond to other actors’ opinions. The moderator ensured that all actors had the chance to contribute equally to the role play. Although it has to be acknowledged that the actors contributed differently due to their different personalities and role. After the role play, the proponents of the group discussed the outcome of the role play with respect to importance of the different actors and their interconnectivity between actors.

During two 6-h seminars, which took place in the month after the 2-day off-campus retreat, the group went through a phase of intense reflection in order to answer the research question stated above. The first seminar was spent on finding a way to visualize the involvement and role of each discipline in regard to the three locations for wind farm construction. The group decided to develop a conceptual model. In the second seminar, the group created the conceptual model. All group proponents took part in the seminars, but the group did not monitor whether or not all proponents contributed equally to the final conceptual model.

Phrasing an integrated research question—Phase 1

The integrated research question was phrased during group meetings, which took place in monthly intervals during the first 9 months of the project (Table 2 ). Informal joint lunch breaks formed the first platform of the group meetings. It was observed that doctoral students and postdoctoral researchers exchanged private and professional topics during the lunch breaks without paying much attention to the disciplinary perspectives. Proponents had the time and space to explain misunderstandings that arose from the conversations throughout the group. It was observed that the group dynamics changed over time. During the first lunch breaks, proponents were mostly interested in private topics or in professional topics related to their own disciplines. At the end of the 9 months period of phase 1, it was observed that the proportion of professional topics that were not related to their own disciplines increased. This shows that the informal lunch breaks nurtured interdisciplinary emphasis of the group.

Formal seminars formed the second platform of the group meetings. During the first formal seminar, the proponents brainstormed about topics relevant to the coastal environment and created a mind map. For the present study, this mind map was visualized as perspective map in Fig. 1 . Relevant topics to the coastal environment considered by the group included, but were not limited to, wind energy production, food production, tourism and residential areas, industry and infrastructure, waste water disposal and dredging, marine resources, and underwater cultural heritage. The proponents divided the coastal environment into three areas of interest, namely (1) onshore near the coast, (2) offshore within territorial sea (up to 12 nautical miles offshore), and (3) offshore within exclusive economic zone (up to 200 nautical miles offshore). After an intense discussion and numerous refinements, the research group decided that the challenge of increasing the proportion of wind energy production within the next decades would probably be the most relevant topic for interdisciplinary research in the three areas of interest today (Deutscher Bundestag, 2014 ; Ender, 2017 ). Therefore, the commonly agreed research problem was framed on understanding the complex roles and interactions between disciplines when searching for an appropriate coastal wind farm location. During phase 1 it was observed that for the proponents it was of particular importance to be able to identify themselves with the chosen research problem with respect to their disciplinary background and to share their expertise with the group.

figure 1

The dark-shaded area highlights the three different locations for wind farms, which are the commonly agreed research areas of the interdisciplinary group work. The three areas include a – c offshore within exclusive economic zone (EEZ), offshore within territorial sea (TS), and onshore near the coast. Alternative suggestions for research areas include d farming, e tourism and residential areas, f industry and infrastructure, g waste water disposal, dredging, and dumping, h scientific surveys, i underwater cultural heritage, j marine resources, and k fishery.

The post-graduates presented their literature research about one aspect of the common research problem (seminars 2–6). At this stage, the definition of a specific research question was not the premier goal of the group. The group was more concerned with the establishment of a common understanding of interdisciplinary aspects within the research problem. It was observed that the aspects chosen by the proponents still remained in their own disciplines during this phase. For example, biologists chose to read literature about bird collision within offshore wind farms or whether or not noise emission would affect the behaviour of marine mammals. A geoscientist was more concerned about the possible difficulties of predicting the sediment stability around wind turbines located in a highly dynamic environment. A social scientist read literature about public perception of onshore and offshore wind farms, whereas a legal scientist studied the differences of regulatory frameworks of wind farm constructions between the different areas of interest. In the following seminars, the proponents seemed to become more familiar with the other disciplines in the group and, but more importantly, appeared to develop an interest to understand the other disciplines’ arguments and concerns. We believe that this transformation towards interdisciplinary group work was mainly initialized by the exchange of personal and professional opinions during the informal lunch breaks.

The phrasing of a commonly agreed research question (seminars 7–9) turned out to be a long-lasting process. Proponents discussed about topics such as the usefulness of phrasing a single integrated research question or the general thematic focus of the question. The hierarchy of words were a matter of discussion too. Proponents argued about, for example, whether or not the order of disciplines as phrased in the research question (“How do natural, social, and legal disciplines […]”) would refer to some kind of a hierarchical order. It was observed that proponents with social and legal professional backgrounds were more actively focusing on levelling the hierarchy of disciplines than the natural scientists. This was probably because social and legal disciplines formed the minority within the group, felt underrepresented, and attempted to strengthen their position.

During the process of phrasing a common research question, the group decided to name the group project InterWind , being a word combination of interdisciplinarity and wind farms. The title of the group project was initially suggested by one of the doctoral students and was later commonly accepted by the entire group. The authors believe that establishing both a common research question and a project name was the most important step for the proponents to identify themselves with the research project. This was especially important because the doctoral students performed the group project also during the last year of their Ph.D. and were therefore preoccupied with other topics.

Creating a common understanding—Phase 2

Multidisciplinary group work was used as a tool to improve the understanding of the different disciplines with respect to the common research question and to encourage interdisciplinary thinking. The proponents of the subgroup approached interdisciplinary thinking from different perspectives. On the one hand, the expert functioned as mentor and could observe and comprehend the other proponents’ struggles and difficulties when facing an unrelated discipline. On the other hand, the proponents of unrelated disciplines enjoyed the immediate benefit from explanations and advices provided by the expert in cases of misunderstandings. The exchange of these two different perspectives within subgroup encouraged interdisciplinary thinking.

The multidisciplinary subgroups presented their findings to the entire group during the first day of the off-campus retreat. In the presentations, the subgroups focused not only on their acquired knowledge but also on their impressions and personal experiences during the multidisciplinary group work. For example, one of the multidisciplinary group presentations focused on how the procedure of wind turbine construction differs throughout the three areas of interest. Among other aspects, it was presented that the type of foundation may differ from a surface foundation in the onshore environment to monopile and tripod foundations in the offshore environment. For the present study, this example was visualized in the lower panel of Fig. 2 . The differences between these three types of foundations were presented from legal, geoscientific, and biological perspectives. The subgroup did not find any societal topics related to the type of foundations. Another subgroup presented the impact of wind turbines on bird migration. The proponents showed that in the public perception, collision with wind turbines as a consequence of bird migration is considered a major obstacle for the construction of wind turbines (Devlin, 2005 ). However, recent systematic studies showed that birds tend to avoid the wind turbines and that the thread for collision is highly overestimated in the public (Hüppop et al., 2006 ).

figure 2

It represents the different weighting (circle size) and interactivity (arrow width) of the four disciplines in the context of wind farm construction between a offshore within the executive economic zone, b offshore within the territorial sea, and c onshore near the coast.

These discrepancies between disciplines observed in the group presentations were vividly discussed by the group. The group decided to class the discrepancies with respect to the three areas of interest (onshore near the coast, offshore within territorial sea, and offshore within exclusive economic zone). In the following, the authors describe the main findings the group made about the differences between disciplines with respect to the three areas of interest.

In the onshore environment near the coast, the group considered geological and biological environmental constraints lower in importance compared with the offshore areas. The main reason for this consideration was that, because onshore wind turbines are commonly built in anthropogenically modified areas, they commonly require simpler ground investigations and have a limited effect on the ecosystem. In contrast, the group considered the impact on society, represented by for example land owners and tourists, as comparatively large (Wolsink, 2007 ). The group explained this conclusion with the high visibility of onshore wind turbines. In areas where the available space is already limited, people may object the construction of wind turbines despite the numerous positive effects on environment and economy.

In the offshore environment, the group discussed that various geological aspects, such as the presence of strong wind and hydrodynamic loads, the sediment properties of the subsoil, and the wind turbine design, have to be accounted for (BSH, 2014 ). Biological aspects include the possible effects of wind turbines on the marine ecosystem (Desholm and Kahlert, 2005 ; Elmer et al., 2007 ) as well as long-term climate variability due to reduction in carbon dioxide emission (Kempton et al., 2007 ). The group considered societal aspects high within the territorial sea as the tourism industry and public acceptance may be influenced in cases where offshore wind farms are visible from the coast (Devine-Wright and Howes, 2010 ; Gee, 2010 ). In the exclusive economic zone, societal impacts are mainly limited to shipping industry and fishery (Berkenhagen et al., 2010 ). Due to the large distance from the coast, offshore wind farms are generally more accepted by coastal communities and negative effects on coastal tourism are low (Hübner and Pohl, 2016 ; Hübner and Pohl, 2014 ). Therefore, the group considered societal aspects smaller in the exclusive economic zone compared with the territorial sea. The legal aspects, such as the regulatory framework for wind farm construction in Germany (BSH, 2014 ) was considered as equally important throughout the three areas of interest.

The group further focused on comparing the interactions between disciplines within the three areas of interest. The group considered that the society emphasizes with fauna and flora more easily than it does with practical aspects of geology, such as geotechnical engineering efforts when searching for a wind farm location. Therefore, the group weighted interactions between society and biology higher than those between society and geology. The highest interactions were considered to exist between society and biology when wind farms form part of the landscape (onshore and offshore within territorial waters) (Gee, 2010 ).

Establishing an interactive communicative framework—Phase 3

In the third phase, the group performed a role play in order to transfer the integrated knowledge gained from the group presentations into an interactive communication framework (second day of the off-campus retreat). The role play was framed in an open forum in which stakeholders from one of the four disciplines discussed where to construct a future fictional wind farm. The roles’ opinions reflected various aspects of the decision process of wind farm constructions and encompassed, among others, a local resident, a wind farm operator, an eco-activist, a federal politician, and an employee working for a federal maritime agency. During the role play the proponents had to emphasize with their new role and built a line of argumentation based on their role’s best interest. As the communication proceeded, the actors emphasized with the perspectives of the other roles, made compromises, and finally decided on a wind farm location every actor could agree upon.

The group went through intense discussions and reflections about the group presentations and the role play in order to find and agree on an integrated answer to the common research question. The group agreed that the complex roles of disciplines and interactions between disciplines with respect to the three areas of interest could be best synthesized by means of a conceptual model (Fig. 2 ). The group decided that the conceptual model should be divided into the three areas of interest. Each subdivision should consist of four geometrical shapes each of them representing one of the four disciplines. The size of geometrical shapes should reflect the group’s decision about the dominance of individual disciplines over other disciplines in the area of interest, respectively. Arrows of different widths would connect the four geometrical shapes in order to visualize a degree of interaction.

The group agreed that the legal framework provides the basis of interactions between the other three disciplines. Therefore, the law discipline was put into the centre (or heart) of the conceptual model. A triangular shape was chosen for the law discipline, symbolizing a cogwheel that drives the interactions and communications between the other disciplines. The other three disciplines were symbolized as circular shapes that surround the law triangle. The circular shape was chosen to be different from the law triangle, but without taking any other meaning into account. Note that the relative position and colour of circles do not indicate any hierarchical order of the disciplines but were chosen solely for a better illustration of the conceptual model.

The relative weighting of the disciplines and their degree of interaction were subject to long discussions throughout the research group. The final conceptual model (Fig. 2 ) was the result of various refinements that were made by all group proponents of all four disciplines and may therefore be considered as a truly interdisciplinary outcome. The conceptual model could indicate weaknesses in current practices and involvements of disciplines regarding wind farm constructions.

Practical guideline for interdisciplinary research process

All observations made during the interdisciplinary group project were synthesized into a practical guideline (Fig. 3 ) that may help other research groups composed of various disciplines to engage in an interdisciplinary problem. The practical guideline is conceptualized as a sequence of three phases of interdisciplinary integration: (1) comparing disciplines, (2) understanding disciplines, and (3) thinking between disciplines. The basic concept of these three phases follows the suggestions made by Lang et al. ( 2012 ) for transdisciplinary research process, who divided integrative research process into (1) problem framing and team building based on a societal and/or scientific problem, (2) co-creation of solution-oriented transferable knowledge, and (3) (re-)integration and application of created knowledge in both societal and scientific practice. The conceptual model of Lang et al. ( 2012 ) has many similarities to other models in the literature (Carew and Wickson, 2010 ; Jahn, 2008 ; Krütli et al., 2010 ; Talwar et al., 2011 ) and was adopted by numerous researchers (Brandt et al., 2013 ; Mauser et al., 2013 ; Miller et al., 2014 ).

figure 3

Geometric objects (triangle, square, circle, and diamond) indicate different disciplines. The term ‘dark cloud’ refers to an unresolved challenge that has to be encountered interdisciplinarily.

The conceptual model synthesized in the present study (Fig. 3 ) starts with phase 1: Comparing disciplines. Doctoral students and/or postdoctoral researchers, originally having professional backgrounds in a single discipline, form a group and collect ideas about a common research problem through group meetings. A commonly agreed research problem is framed through iterative refinements throughout the group proponents, before the group decides upon an integrated research question. In phase 1, proponents may face problems and misunderstandings when trying to emphasize with the other disciplines. The limited understanding about the other disciplines is illustrated in the conceptual model by a dark cloud , which every proponent of the group must enter in order to find an integrated research question (as symbolized in the left part of Fig. 3 ). Group meetings that combine informal lunch breaks with subsequent formal seminars were found to be a successful tool for helping proponents to compare disciplines, to collect ideas for a research problem, which does not privilege one discipline over another, and to finally reach a common agreement on an integrated research question.

Lang et al. ( 2012 ) emphasized that the individual phases of transdisciplinary research process are not likely to be a linear process but often have to be performed in an iterative manner in order to reflect about transdisciplinarity. Based on the methodological approach of the present study, the three phases of the practical guideline followed a predefined chronological sequence, without allowing any iterative adjustments between phases. However, within phase 1 an iteration step was included that allowed a refinement of the common research problem.

In phase 2, the group establishes a common understanding of the different disciplines through multidisciplinary group work. The different perspectives of expert and non-experts during multidisciplinary group work nurtures empathy of proponents when dealing with unknown disciplines. The proponents familiarize themselves with an unknown discipline during their own literature review, can discuss and change perspectives during the preparation of multidisciplinary group presentations, and can finally benefit from listening to and discussing about other presentations being held in an atmosphere not related to normal work environment, for example during an off-campus retreat. During this process, each proponent enters the dark cloud of disciplines, previously considered to contain research fields largely unrelated to each other, to steadily form an interconnected transdisciplinary framework (as symbolized on the left side of Fig. 3 ).

In phase 3, the group discusses and reflects about the findings with respect to the integrated research question through an interactivity, for example a role play. The answer to the integrated research question should reflect the ability of the group for successful interdisciplinary work. An abstraction, for example using a conceptual model, could be a helpful way to reduce complexity and ensure an answer that can be accepted by the entire group. During this process, the proponents finally start to understand the integrated problem as multidimensional complex of disciplinary interrelations and learn to think at the interfaces between disciplines (as symbolized on the left side of Fig. 3 ).

Our practical guideline for approaching an interdisciplinary problem may be considered as an extension to the conceptual model for transdisciplinary research process of Lang et al. ( 2012 ). It largely follows the three proposed phases of research process but also incorporates five new concepts, namely group meetings, multidisciplinary group work, an off-campus retreat, an interactivity, and an abstraction of interdisciplinarity that enable the research group to approach an integrated problem interdisciplinarily.

Our practical guideline is endorsed by the five principles of interdisciplinary collaboration presented by Brown et al. ( 2015 ). Here the researchers initially undergone through a phase of “forging a shared mission”, which provided an overall goal of collaboration. The shared mission needed to be formulated broad enough to incorporate meaningful roles for all disciplinary researchers involved. This principle was also observed by us during the process of phrasing an integrated research question in phase 1 as shown in our practical guideline. Brown et al. ( 2015 ) further described the usefulness of “T-shaped researchers” (Hansen and Von Oetinger, 2001 ). Such researchers are reported as experts in their own discipline, but are also capable of looking beyond their scope. In our practical guideline, the development of T-shaped researchers was nurtured through the multidisciplinary group work in phase 2 and the interactivity in phase 3. By this, the students have transferred into T-shaped researchers. In particular, by learning that an active engagement with other disciplines is important, and hence, understanding and appreciating their norms, theories, approaches, evolved as an important step towards interdisciplinary collaboration.

We believe that our practical guideline will help others facing similar challenges of interdisciplinarity and we are looking forward to future initiatives that incorporate the practical guideline into their interdisciplinary education. Nonetheless, we think that our presented guideline describes a practical approach to transform a disciplinary thinking group to an interdisciplinary working team efficiently.

Advantages and challenges of interdisciplinary group work

The interdisciplinary group project revealed a number of issues that are common among other interdisplinary and transdisciplinary working groups. The group project was biased in terms of disciplinary diversity. The majority of proponents had a background in natural sciences, while only few proponents came from social and legal science disciplines. The bias between disciplines arose from the relatively small number of participants, which possibly affected the weighting of one discipline over another during the three phases of interdisciplinary integration, especially during the multidisciplinary group work. Asymmetry in interdisciplinary integration was also mentioned by Viseu ( 2015 ). She pointed out that social sciences are often brought into a research team after the project already have been started. Moreover, social scientists sadly form the minority, lack in independence and funding, which eventually leads to a hampering in knowledge production. For future interdisciplinary group projects, we recommend that all disciplines are equally involved during all phases of interdisciplinary collaboration. This will avoid problems related to an unequal distribution and weighing of disciplines within the research group.

Communication problems on topics of mutual interest is famously and anecdotally a common problem in interdisciplinary collaboration. While the general challenges as well as the benefits have long been recognized (Brewer, 1999 ; Nissani, 1997 ), we would like to discuss some of the struggles that came with this project with concrete examples. The problems we encountered fall broadly into three categories: language (definition of terms, implicit assumptions), form (writing style, structure, organization), and prejudice (overcoming of stereotypes).

Language-based problems primarily appeared where certain words have different definitions in colloquial language and the technical terminology of one of the scientific disciplines. While this was rarely the cause for complete misunderstandings, it often led to lengthy discussions about the phrasing in written form. An example is the word coast : In casual conversation, it is more or less synonymous with beach or shore and can be understood as where the land meets the sea ; this would also be the definition most people would use in an interview for a sociological study. Geological definitions of the coastal system include significant portions of the continental shelf up to the shelf break, as well as inland areas that are still affected by coastal processes, for instance by dune formation. For legal purposes, distinctions are made between land, territorial waters, exclusive economic zone, and international waters. These kinds of discussions are an important part of the interdisciplinary process and a sufficient amount of time should be set aside for them.

Formal problems arose when decisions had to be made about content and order of information, both in the oral presentations and during the preparation of the present paper. Natural sciences make extensive use of graphical forms of presentation in the form of diagrams and sketches—a rarity at best in legal sciences, which in turn make good use of footnotes for clarification and additional information. Differing viewpoints exist about the need to quantify data or the appropriateness of qualitative descriptions. The order in which information is presented greatly influences the focus set for the audience. The audience itself is also a decisive factor; especially a mixed audience of experts from different fields has very heterogeneous expectations that can hardly be satisfied all at once. For the present paper, decisions were made regarding style, structure, significance of findings, and even writing conventions like first vs. third person and formal tone.

Prejudice might come as an unexpected challenge. Post-graduates of their various disciplines have been trained in the environment of a certain academic culture that they tend to identify with. This includes to distinguish their own discipline from others, often in the form of humorous observations about their aims, practices and usage as well as the perceived ranking of the respective disciplines on a scale of scientific value (with their own discipline of course close to the top).

Later in their career, when post-graduates become experts, they find themselves in a position where they need to justify their research in competitive environments, including the frequent search for future funding or constant rate of publications in a high-impact journal. By necessity, they learn to present their work in a way that highlights its values. Although few scientists will consciously think lesser of their colleagues, some may fall into the trap of unconsciously evaluate other disciplines less favourably than their own. From the observations made in the present study, the reason for bringing disciplines together is not to make scholars experts on all things (a rather hopeful goal) but to enable them to collaborate on a shared and integrated question. Apart from knowledge exchange itself and learning from each other, it is important that they trust each other’s expertise. Perhaps the most important thing to highlight is that post-graduates need to learn how to engage with other disciplines. This line of thinking is further supported by unfamiliarity with the tools and premises of said disciplines and is especially present in interdisciplinary environments where hard and soft sciences are part of the same group project. In this way, interdisciplinary projects can provide unique benefits, both to the work itself by enabling a greater inclusiveness and the ability to recognize more facets of a problem, as well as to the persons involved by broadening their horizon and facilitating scientific exchange.

One success of group projects, such as the one of the present study, is that it provided time and space for such conversations and argumentations. Without working through a structured process on this case study, the opportunity would have never arisen to learn about important differences between disciplinary structures and methods. Nor would most proponents of the group have a chance to examine their own assumptions about scientific vocabulary and consider alternate meanings of basic terms. These encounters and moments were only made possible through the group project, which proved its value in training early career scientists to work cooperatively across disciplinary boundaries. Overcoming these problems requires the willingness to compromise. The potential downside can be a loss of precision in some aspects of the work, which has to be pointed out and balanced by references to specialized literature.

The present study reports findings about an interdisciplinary group project in which doctoral students and postdoctoral researchers with natural, social, and legal professional backgrounds faced challenges of interdisciplinarity. Results of the group project include in a practical guideline, which extends existing conceptual models about transdisciplinary research process by introducing a concept that helps research groups to approach an integrated problem interdisciplinarily. In synthesis, the group went through three phases of interdisciplinary integration, namely (1) comparing disciplines, (2) understanding disciplines, and (3) thinking between disciplines.

A group of doctoral students and postdoctoral researchers collect ideas about a common research problem through group meetings and frame an integrated research question by iterative refinements. Group meetings combine informal lunch breaks with subsequent formal seminars and were found to be an effective too for helping proponents to initiate interdisciplinary thinking.

A common understanding about the different disciplines’ perspectives is established through multidisciplinary group work of experts and non-experts. The different perspectives of expert and non-experts during multidisciplinary group work nurtures empathy of proponents when dealing with unknown disciplines. Group presentations and subsequent discussions in an atmosphere not related to normal work environment help to steadily form an interconnected transdisciplinary framework between disciplines.

The group discusses and reflects about the findings with respect to the integrated research question through an interactivity, for example a role play. The answer to the integrated research question should reflect the ability of the group for successful interdisciplinary work. An abstraction, for example using a conceptual model, could be a helpful way to reduce complexity and ensure an answer that can be excepted by the entire group.

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Acknowledgements

This research was funded by the DFG Research Center MARUM of the University of Bremen, Germany, through INTERCOAST (Reference number: 112807311) and the University of Waikato in Hamilton, New Zealand. We also acknowledge K. Huhn who encouraged this project. We thank T. Kulgemeyer who contributed to the discussion of this manuscript and for providing comments on the final manuscript. We acknowledge B. Blossier, F. Boxberg, C. Gawrych, S. Gustafson, M. Preu, and F. Staudt who commented on an early version of this manuscript. We thank all proponents who contributed to the group project InterWind.

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Kluger, M.O., Bartzke, G. A practical guideline how to tackle interdisciplinarity—A synthesis from a post-graduate group project. Humanit Soc Sci Commun 7 , 47 (2020). https://doi.org/10.1057/s41599-020-00540-9

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Interdisciplinary Problem Solving in Human Dominated Wetland Ecosystems

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Understanding and solving environmental challenges increasingly requires a combination of expertise from across multiple disciplines.

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This Research Experiences for Undergraduates  ( REU) program emphasizes engaging students in interdisciplinary earth systems research that builds collaboration and communication skills for solving complex environmental problems. We will use wetland restoration as a lens through which participants will get hands-on experience in studying the interactions between science and society that shape ecosystem functions and services.

Projects will be grouped into interdisciplinary research clusters that include the following approaches: (i) biogeochemistry and ecology, (ii) sociological feedbacks, and (iii) geospatial patterns and scaling. This summer research experience centers around four key components (1) mentored research projects, (2) interdisciplinary skill development, (3) professional development workshops, and (4) scientific communication and outreach.

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REU participants receive a stipend of $6000, free on-campus housing in RIT’s Global Village, meal allowances and a travel stipend. Additional funding is available after the summer session ends for some students to travel to conferences to present their research.

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This program is for students who are enrolled in a college or university, including community college, and who will not yet have completed their undergraduate degree by the end of the summer session. This program is particularly well suited for students who will be completing their second or third year and who have limited access to earth science or environmental science research opportunities at their home institution. This is an interdisciplinary program and we welcome participants from all academic majors who are interested in environmental science questions. We encourage women, members of under-represented minorities, and deaf or hard-of-hearing students to apply. NSF funding requirements restrict participation to students who are US citizens, US nationals, or permanent residents.

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Students will participate in weekly workshops focusing on both interdisciplinary research skills and professional development. Skills workshops are designed to expose all participants to the wide range of approaches that are used in interdisciplinary earth science research, with topics ranging from sequencing to remote sensing. Professional development workshops will focus on scientific best practices and communication skills and will culminate in activities where students will disseminate their research in a range of settings, including youth programs, community events, and RIT’s undergraduate research symposium.

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Freshwater wetlands are valued for services that include carbon sequestration, nutrient removal and habitat provisioning, but are also significant sources of greenhouse gases. As landscapes rapidly urbanize these ecosystems are increasingly beset by complex environmental forces (land-use change, pollution, species invasion, climate change, etc…) that need to be addressed with interdisciplinary approaches that consider sociological interactions and landscape heterogeneity.

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The relationship between extrinsic and intrinsic drivers and ecosystem structure and function. This project builds on ongoing collaborative research efforts that seek to understand the structure and function of wetlands along an urban-rural gradient. Interdisciplinary research clusters will address the relationship between land-use history and emergent functions in created wetlands and how surrounding land-use impacts wetland function and success.

The role of ‘top-down’ vs ‘bottom-up’ control over ecosystem function and restoration success in constructed wetlands. This project utilizes ongoing large-scale manipulative experiments to disentangle interactions among ‘top-down’ and ‘bottom-up’ drivers and their impact on wetland structure and function. Interdisciplinary project clusters will utilize experiments that address the impacts of grazers and soil amendment on wetland function and restoration success. The utility and limits of assessing and scaling ecosystem structure and function in small, heterogeneous wetlands. This interdisciplinary project focuses on research gaps in our understanding of how to characterize and scale ecosystem properties in complex, heterogeneous habitats embedded within human dominated landscapes. Students will work on integrating field data with remote sensing imagery and modeling approaches. Interdisciplinary project clusters will address questions related to the scale we need to sample a wetland in order to derive useful information about its function and the tools that can be used to assess wetland functions and services.

Within a research cluster, participants will be paired with a mentor and a disciplinary approach. Research clusters listed below:

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Potential projects could approach research questions using tools that include (i) spatial analyses and Geographic Information Systems (GIS) applications, (ii) remote sensing technologies, including fine-scale 3D vegetation structure and hyperspectral imaging statistics, and (iii) data integration, hypothesis testing, and scaling using ecosystem modeling approaches.

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Interdisciplinary Learning in Mathematics and Science: Transfer of Learning for 21st Century Problem Solving at University

Transfer of learning, the application of learning to different contexts over time, is important to all learning for development. As 21st century skills specifically aim to be “generic,” there is an assumption that they can be transferred from context to context. We investigate the process of transfer in problem solving, with specific focus on mathematical problem solving tasks. Problem solving is highly valued in 21st century workplaces, where mathematical skills are also considered to be foundational in STEM and of paramount importance. This study examines the transfer of first semester mathematics learning to problem solving in second semester physics at university. We report on: (1) university students’ (n = 10) “think-aloud” accounts of the process of transfer; and (2) students’ (n = 10) and academics’ (n = 8) perspectives on transfer processes and problem solving. Think-aloud accounts show students’ recursive use of interpretation, integration, planning and execution thinking processes and highlight the meta-cognitive strategies used in transfer. Academics’ and students’ perspectives on transfer show disparities. Understanding these perspectives is important to current initiatives to integrate and optimise 21st century learning within universities. We argue that renewed attention on the concept of transfer is needed if the generic aims of 21st century skills are to be understood and promoted.

1. Introduction

This article focuses on the centrality and potential of transfer of learning for 21st Century (21C). In particular we explore mathematics transfer, which is widely recognised as central to human development, educational systems and economies, and has recently been the focus of many research projects, policy and public campaigns internationally ( Australian Industry Group 2013 ; Office of the Chief Scientist 2013 ; National Research Council 2013 ; The Royal Society Science Policy Centre 2014 ; U.S. Congress Joint Economic Committee 2012 ; U.S. Department of Education 2016 ). However, given its importance, relatively little recent research exists that examines how mathematics learning is applied in other contexts; and even less research that operationalises and explores transfer of learning within authentic educational systems ( Nakakoji and Wilson 2018 ). We argue that considering the transfer of mathematics learning is critical to the development of 21C skills; and that attention to the role of transfer more broadly, within holistic conceptions of 21C learning, is needed to progress understanding in the field.

While looking at transfer between mathematics and physics, we also consider the relationship between mathematics and science more broadly within the context of university. The importance of this relationship to industry and society is undisputed; and learning in these disciplinary areas is seen as key to 21Century education. Yet, within universities, optimal productivity at the nexus between mathematics and science is often assumed and rarely examined. Most universities, for example, offer mathematics “service courses” to a wide range of degree programs and assume that learning in mathematics courses is effectively applied across degree curriculum. However, sparse research is published to support this assumption and less still that can inform practice in interdisciplinary teaching and learning ( Nakakoji and Wilson 2018 ).

This paper makes two contributions. First, we examine students’ think-aloud accounts of the processes they use in transfer of learning tasks. Second, we examine both student and academic staff perspectives on the mathematics/science relationship, including their views on factors that promote or hinder transfer.

We take a process approach to examine transfer of learning, in relation to 21C problem solving skills. The process-oriented approach ( Sternberg 2000 ) aims to “isolate a finite set of competent that can be combined in various ways to perform any cognitive task” ( Meichenbaum 1980, p. 271 ). We adopt a think-aloud protocol to collect data and document the cognitive processes used in transfer of mathematics learning to science problem-solving tasks. This approach, in tandem with post-task interviews, has strong practical relevance to education because it can identify barriers and stumbling blocks to student learning.

This small-scale exploratory research is nonetheless significant given the historical relationship between mathematics and science and the relative lack of research attention given to this important interdisciplinary relationship. The study makes a preliminary contribution to applied understanding of what can be done to promote the transfer of learning for 21C skills at this important disciplinary nexus.

1.1. Transfer and 21st Century Learning

Rapid technological advancement has changed the skills and knowledge used in workplaces. The change requires employees to process various types of information, analyse big data, interact with and communicate with people, and apply their prior knowledge and experience to a new situation to solve the complex problem in different contexts. Industries demand graduates with generic skills, such as higher-order critical thinking and problem solving, and also strong metacognitive and communication skills. According to the Organization for Economic Co-operation and Development (OECD), 21st century skills refer to “skills and competencies young people will be required to have in order to be effective workers and citizens in the knowledge society of the 21st century” ( Ananiadou and Claro 2009, p. 8 ). Problem solving is consistently identified as central to these requirements, and routinely listed as a desirable graduate attribute for employability and as an integral component in 21C learning.

Transfer is viewed as critical for future education and central to the application of 21st century skills ( OECD 2018 ). Logic dictates that transfer is needed for the application of these generic and “transferable” skills—although this has not, as yet, been as widely acknowledged in the academic literature as might be expected. With predictions of rapidly evolving environments in our future, the ability to apply prior learning to new contexts is essential. Transfer of mathematical learning is the ability of students to apply mathematical skills, knowledge, and reasoning to other disciplines, and this is likely to be particularly important to 21st century skills. Demonstration of this ability is a central issue in mathematics and science education ( Tariq 2013 ; King et al. 2015 ).

1.2. The Relationship between Mathematics and Science

National reports suggest many countries are concerned about participation, standards, and capacity building in mathematics and science for STEM related industry and labor markets ( Australian Industry Group 2013 ; National Research Council 2013 ; Office of the Chief Scientist 2013 ; The Royal Society 2014 ; U.S. Department of Education 2016 ). This has increased research focus on education in these fields. However, learning in these fields is not discrete, they are intertwined, and we need to know more about how learning in mathematics and science is transferred and shared. The study presented here is part of a larger project exploring learning at the mathematics/science nexus at one Australian university ( Nakakoji and Wilson 2014 , 2018 ; Nakakoji et al. 2014 ).

Interdisciplinary relationships between mathematics and science are critically important as mathematics is applied in diverse disciplines and these relationships lead to advancements across disciplines ( National Research Council 2013 ). Both mathematics and science educators need to consider the implications of this interdisciplinarity in terms of effective teaching and learning in schools and universities. For example, U.S. primary and secondary education standards require teachers to enhance the synergy between these disciplines by application of mathematics to science, e.g., mathematical modelling and statistics ( Stage et al. 2013 ). The close relationship is also evident in the fact that mathematical learning in high schools and university is a strong predictor of attainment in science; with mathematics scores explaining between 43 and 87 percent of the variance in a range of science subjects ( Nakakoji and Wilson 2014 ; Sadler and Tai 2007 ). Furthermore, there is currently a flux of research into STEM and internationally many universities conduct educational research in these disciplines in order to improve their teaching ( King and Sen 2013 ). As part of this, it is widely recognised that effective communication and collaboration between academics in STEM disciplines is important ( Anderson et al. 2011 ; Blumberg et al. 2005 ; Orton and Roper 2000 ).

The strong correlations found between mathematics and science learning ( Nakakoji and Wilson 2014 ; Sadler and Tai 2007 ) can be attributed to both the underlying shared general and specific intelligence factors ( Nisbett et al. 2012 ) and the transfer of specific learning and skills between these disciplines ( Roberts et al. 2007 ; Nakakoji and Wilson 2018 ), including, for example, the common and shared use of problem solving schemata and other cognitive strategies. The g factor or general ability is the underlying foundation to diverse cognitive abilities that directly or indirectly affects all learning, including in mathematics and science. According to Cattell-Horn-Carroll theory, g is the strongest factor analytical construct in the hierarchical model of intelligence (see for example, Taub et al. 2008 ). Furthermore, the g factor has been shown to be highly correlated with international assessments of educational attainment, such as PISA and TIMSS, and IQ tests ( Rindermann 2007 ). When examining two different educational attainments it is unsurprising to see high levels of correlation, due the fact that both will draw on the g factor.

The relationship between mathematics and general ability has been examined empirically and is particularly strong. The g factor was correlated with 25 secondary school subjects in the UK; and mathematics had the strongest association with g (r = 0.77), explaining approximately sixty percent of the variance in general ability ( Deary et al. 2007 ). This suggests that among educational attainments, mathematics is particularly linked to g and that this might also explain how mathematics would be a strong predictor of other educational attainments.

Complicating this picture of correlated educational attainments is the unique relationship between mathematics and science. As these are highly cognate disciplines, they may share additional factors. These may contribute to g, and they may also be specific intelligence factors which explain additional achievement variance beyond g . Taub and colleagues (2008) have demonstrated how cognitive ability factors, including fluid reasoning and processing speed, are highly associated with mathematical attainment. Science and mathematics problem-solving assessments may share requirements for fluid reasoning and processing speed (which contribute to g). Science and mathematics may also both draw on specific factors, like gq (quantitative knowledge), so we might expect higher levels of correlation between them than between other, less cognate disciplines. We might also expect these factors to be drawn on in transfer of learning tasks ( Richmond et al. 2011 ) and in problem solving tasks ( Decker and Roberts 2015 ).

1.3. University Mathematics Service Courses and Science Courses

In many countries, including Australia, universities and higher education institutions adopt a “service course” model. In this model, first year mathematics courses are provided by mathematics departments to students from diverse STEM disciplines, such as biology, chemistry, engineering, and physics. These courses cover calculus, differential equations, and linear algebra; and can be offered at different levels (fundamental, intermediate, advanced) according to these students’ prior learning in high school mathematics and the requirements for their degree programs (see for example Nakakoji et al. 2014 ). As sciences are viewed as mathematically cognate disciplines, many institutions have mandatory requirements for science students to study first year mathematics service courses where mathematical skills, knowledge, and reasoning are developed so that they can be applied in other disciplinary learning. This approach often goes unchallenged, and yet it is built upon a range of assumptions, including: (i) students engage with service courses and effective learning occurs; (ii) skills and understanding in mathematics service courses are transferred to other disciplines; (iii) transfer of skills and understanding is assessed in other disciplinary areas, and (iv) the mathematics learnt in service courses is useful in building 21C skills for professions and working life. Interrogating these assumptions within one Australian university, we examined the correlation between mathematics and science learning ( Nakakoji and Wilson 2014 ; Nakakoji et al. 2014 ) and looked for evidence of transfer of learning between them. Using extant university exam assessments, we were able to demonstrate transfer of learning in some science courses, but not others, dependent on the requirements for mathematical reasoning and calculation evident within the course assessments ( Nakakoji and Wilson 2018 ). Specifically, we found assessment of mathematical learning and transfer of learning was evident in only engineering and physics course assessments; and mathematics was not assessed in biology and biochemistry courses in a way that enabled testing and demonstration of transfer from the mathematics service courses.

The larger project employed mixed method design to explore transfer of mathematical learning in a range of different ways (see Nakakoji et al. 2014 ), and findings demonstrated the importance and predictive power of mathematics to the subsequent learning in university science ( Nakakoji and Wilson 2014 , 2018 ). In Nakakoji and Wilson ( 2014 ), multiple regression analysis confirmed strong relationships between mathematics and science attainment. For example, 84% of the variance in second semester biology was explained by first semester biology and mathematics marks; with mathematics uniquely explaining 3.8 percent. In addition, in Nakakoji and Wilson ( 2018 ), transfer was quantitatively measured, using a previously established Transfer Index ( Roberts et al. 2007 ). Our research applied this index to pre-existing university tests and exam data; demonstrating, for the first time, that these can be used to assess transfer. We were able to identify transfer of mathematics learning to physics and engineering; however, in biology and biochemistry, the papers provided no opportunity to assess transfer. Path analyses showed significant direct-transfer effect in the advanced physics course, i.e., the final marks in the course increased by a 0.67 standard deviation when transfer was increased by one standard deviation. This analysis relied on data from marks in exams and demonstrated the importance of mathematics to science learning; however, it did not examine the processes of transfer.

In this paper, we report on students’ think-aloud accounts of transfer and the views of students and academics from across this range of science courses. We focus specifically on a problem-solving physics task requiring mathematical curriculum from the service courses. This sort of challenging task requires complex higher-order thinking and utilization of problem-solving schemata.

The study presented here examines the processes involved in transfer, using a think-aloud method ( Van Someren et al. 1994 ) to see how students articulate their thinking while attempting a task requiring transfer (task details are provided later). Think-aloud is a method that can be used to explore and examine the cognitive processes of thinking by asking participants to constantly verbalise what they are thinking while doing assigned tasks ( Van Someren et al. 1994 ). Think-aloud reports provide rich information on cognition during complex problem solving tasks ( Lohman 2000 ). This method was originally used in psychology (ibid.), and is currently applied in education, psychology, and learning science research on metacognitive skills ( Bannert and Mengelkamp 2008 ), collaborative problem solving ( Siddiq and Scherer 2017 ), the process of schematic representation ( Anwar et al. 2018 ), web-based learning ( Young 2005 ), and science text and diagrams ( Cromley et al. 2010 ). However, in relation to mathematics education, there are few studies using think-aloud (e.g., Brennan et al. 2010 ; Ke 2008 ; Monaghan 2005 ) and these papers focus on primary school mathematics. We were unable to find prior research using the think-aloud method in the context of university mathematics education.

Analysis of the think-aloud accounts is framed by mathematical problem-solving theory ( Mayer 1992 ; Okamoto 2008 ; Seo 2010 ) and also Bloom’s taxonomy ( Bloom et al. 1956 ). We also use qualitative data from interviews to further explore student and academic perspectives on problem solving, transfer, and the broader relationship between mathematics and science learning. We address the following research questions:

  • What are the processes of transfer (if any) evident in students’ “think-aloud” accounts while solving physics exam questions requiring knowledge and understanding from their mathematics service courses?
  • What are the challenges in transfer of learning reported by students and by academics?
  • What teaching and learning factors do students and academics believe to enhance transfer?

2. Literature Review

We present a brief review of literature relating to transfer of learning, including important theoretical explanations of how transfer might occur and what factors are involved in it. The review focuses specifically on the research examining transfer in mathematical and science contexts, although a vast corpus of literature exists on transfer more generally.

2.1. Transfer of Learning

Many researchers in education and psychology have investigated transfer since the late nineteenth century. In a broad sense, as learning involves the application of prior learning to new similar or different contexts, transfer is related to all learning. Accordingly, transfer can be seen as the central or primary goal in education ( Bransford et al. 1999 ; De Corte 1995 , 2003 ; Engle 2012 ; Mestre 2003 ; Siler and Willow 2014 ). The acknowledgment that 21C learning will require adaption to rapidly evolving environments means that transfer of learning is key to efficient and effective education in the future. Teaching and learning that is able to optimise transfer of learning is an important goal. In their account of 21C learning, Saavedra and Opfer point out:

Students must apply the skills and knowledge they gain in one discipline to another and what they learn in school to other areas of their lives. A common theme is that ordinary instruction doesn’t prepare learners well to transfer what they learn, but explicit attention to the challenges of transfer can cultivate it. ( Saavedra and Opfer 2012, p. 10 )

As transfer can be related to all learning, the list of factors associated with it can be extensive and we review here only some indicative starting points. Billing ( 2007 ) identifies nine major factors to facilitate transfer based on his survey of several hundred papers. They are (i) motivation, (ii) metacognitive strategies and skills, (iii) learning in context, (iv) principles, rules, and schemata acquisition, (v) similarity and analogy, (vi) varied examples and contexts, (vii) reduced cognitive load, (viii) active learning, and (ix) learning by discovery. In addition, transfer can be promoted by the necessity of prior knowledge (see Mestre 2003 ) and learning with deep understanding ( Barnett and Ceci 2002 ; Brown and Kane 1988 ; Chi et al. 1989 , 1994 ).

2.2. Cognitive Explanations of Transfer

Undoubtedly, transfer involves higher order thinking ( Anderson et al. 2001 ; Bloom et al. 1956 ), as does mathematical problem solving ( Mayer and Wittrock 1996 ). Consequently, to analyse transfer in the problem-solving task in this study, we use both a framework for mathematical problem solving ( Seo 2010 ) and Bloom’s seminal cognitive taxonomy which outlines lower and higher-order thinking ( Bloom et al. 1956 ).

2.2.1. Higher Order Thinking and Bloom’s Taxonomy

The taxonomy of educational objectives developed by Bloom and colleagues ( Anderson et al. 2001 ; Bloom et al. 1956 ; Krathwohl et al. 1964 ) has been influential in providing the foundations to understand transfer of learning ( Krathwohl 2002 ). Bloom’s taxonomy (1956), which is one of the most seminal books in education ( Anderson 2002 ), presents a framework that is used for various purposes, including assessment, curriculum development, instruction, and learning theory ( Seaman 2011 ). Importantly, Bloom’s taxonomy is a useful tool to understand the different levels of cognitive processes related to higher order thinking. The taxonomy recognises three major domains in cognitive functioning: the cognitive domain ( Bloom et al. 1956 ), affective domain ( Krathwohl et al. 1964 ), and psychomotor domain. The first domain is relevant to this study as it is helpful to classify lower and higher order thinking. The cognitive domain entails “the recall or recognition of knowledge and the development of intellectual abilities and skills” ( Krathwohl 2002, p. 7 ). The taxonomy categorises and ranks increasing levels of higher order thinking: (i) knowledge, (ii) comprehension, (iii) application, (iv) analysis, (v) synthesis, and (xi) evaluation. The taxonomy hierarchy is cumulative and easy to understand, with many examples listed by Bloom and colleagues (1956), a wealth of supporting empirical studies (e.g., Kropp et al. 1966 ; Miller et al. 1979 ), and well established validity ( Seaman 2011 ). A summary, with explanations and some mathematical examples, is shown in Table 1 . Research suggests that learners need to use all these cognitive processes, including the higher order thinking, for effective transfer of learning.

Summary of Bloom’s original taxonomy (1956) with mathematics application examples.

Source: ( Bloom et al. 1956 ).

In mathematics education, Bloom’s taxonomy has been widely used for teaching and assessment for over a half century ( Thompson 2008 ). However, it has faced criticism with claims that the taxonomy fails to distinguish between the different levels of mathematical reasoning ( Suzuki 1997 ). Another issue is its apparent inaccuracy in predicting which of the cognitive processes students use to solve problems in mathematics tests ( Gierl 1997 ). To address these weaknesses when analysing transfer of mathematical learning, we use Bloom’s cognitive taxonomy alongside more specific theory of mathematical problem-solving.

2.2.2. Mathematical Problem-Solving Theories and Transfer

We use mathematical problem-solving theory to analyse students’ accounts of the transfer task to address the criticisms of Bloom’s taxonomy, and also because the nature of the exam questions used in the task is built around a problem-solving approach.

Problem-solving is defined as “cognitive processing directed at achieving a goal when no solution method is obvious to the problem solver” ( Mayer and Wittrock 1996, p. 47 ). Sub-processes of problem-solving cover representation, planning, and executing ( Mayer and Wittrock 1996 ), all of which can involve metacognition which is “cognition on cognition” or “thinking about thinking.” Metacognition enhances student performance via conscious and deliberative problem-solving strategies in planning, monitoring, and evaluating ( Ali et al. 2018 ). In order for successful transfer to occur, a learner selects appropriate previous skills and knowledge, applies them into new problems, and monitors the appropriate general and specific cognitive processes to solve the problems ( Mayer and Wittrock 1996 ).

Literature on mathematical problem-solving falls into three categories ( Okamoto 2008 ): mere calculation problems (e.g., Brown and Burton 1978 ), algebraic word problems (e.g., Kintsch 1986 ), and geometric problems (e.g., Owen and Sweller 1985 ). Metacognition is thought to be more deeply involved with word problems and geometric problems than calculation problems ( Okamoto 2008 ). Some literature provides additional detail on four levels of cognitive processes in this mathematical problem solving (e.g., Mayer 1992 ; Okamoto 2008 ) and a specific model of cognitive sequencing and flow, see Figure 1 , which is translated from Seo ( 2010 ).

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Four levels of procedures of mathematical problem solving. ( Source: Seo 2010, p. 230 translated by Yoshitaka Nakakoji).

In this study, we use Seo’s model as a theoretical framework, alongside Bloom’s taxonomy, to analyse the processes of transfer. Whilst Seo’s model comes from the Japanese literature on mathematical learning, it shows some remarkable similarities to the OECD PISA problem solving assessment framework ( DeBortoli and Macaskill 2014 ). The model covers basic procedural steps, and it was anticipated that this could frame students’ descriptions in their think-aloud accounts. This was considered appropriate for a first pass application of think-aloud to transfer, some of the more complex models of problem solving (e.g., Ichikawa et al. 2009 ) may be appropriate for follow-up research.

In particular, a problem schema is seen as important in mathematical learning ( Okamoto 2008 ; Silver 1987 ) and is defined as patterned knowledge about structures of problems and ways of solving problems ( Seo 2010 ). The schema referred to in Seo’s model is “a cluster of knowledge representing a particular generic procedure, object, percept, event, sequence of events, or social situation” ( Thorndyke 1984, p. 167 ), which is featured by five characteristics: abstraction, instantiation, prediction, induction, and hierarchical organization ( Reed 1993 ). This schema is understood to be especially useful in solving word problems ( Seo 2010 ) and geometric problems ( Okamoto 2008 ). Students with insufficient problem schema may experience mathematical learning difficulties. Many primary and secondary students, although good at calculations, have difficulties with word problems ( Seo 2010 ). This is because they fail to understand the meaning of problems and form the representation of the whole problems ( Seo 2010 ). This is related to the translation and integration procedures in Figure 1 , which involve use of a problem schema. According to schema theory, transfer of learning is heavily subject to whether appropriate anticipatory schemata are activated ( Salomon and Perkins 1989 ). For example, vertical transfer (transfer of basic knowledge to higher level understanding) needs to activate procedural schemata which have been developed previously ( Royer 1979 ).

2.3. Socio-Cultural Explanations of Transfer

Socio-cultural theories of transfer emphasise the importance of social and cultural learning interactions and contexts. In particular, situated learning (for example, see Greeno 2011 ; Greeno et al. 1993 ; Lave 1988 ; Lave and Wenger 1991 ) and the actor-oriented approach to transfer ( Karakok 2009 ; Lobato 2006 , 2008a , 2008b ) are relevant to transfer of mathematical learning. In this study, these perspectives are useful for understanding the context of transfer in mathematics and science education at university. These theories highlight the importance of individuals’ personally constructed learning and the interplay between their understanding of mathematics and the process of solving transfer tasks.

Lave ( 1988 ) first pointed out a paucity of transfer studies in natural settings and academic disregard of the need for problem-solving in daily life situations. Lave’s research on situated learning makes it clear that learners, their thinking, and learning activities are not independent from their contexts, thus “cognition and performance are context-specific, in a fundamental sense” ( Evans 1998, p. 270 ). Greeno et al. ( 1993 ) expanded and articulated the situatived perspective on transfer, emphasising not only the importance of the situation where learning occurs, but also the learner’s ability to interact with other people and the various materials available for learning ( Marton 2006 ). In relation to our study, this perspective acknowledges that while mathematical learning occurs in formal course settings, including university lectures and tutorials with interactions between lecturers, tutors, and peers, but we cannot view this learning as complete. In addition, learning can be constructed when students study by themselves, or with others, at home, in a library, or elsewhere, and by utilising physical and online materials.

In experimental studies, researchers have highlighted difficulties in observing transfer (see for example, Detterman 1993 ; Hatano and Greeno 1999 ; Lobato 2006 ). The sociocultural actor-oriented approach attempts to overcome this difficulty, and some other weaknesses in understanding transfer, by shifting to a learner-centred perspective. This approach specifically examines transfer processes by looking at how learners relate to learning experiences within novel situations. Thus, this approach enables researchers to consider the occurrence of transfer even if students provide incorrect or non-standard performance in tasks, while this situation would be treated as failure of transfer in experimental studies where transfer is measured as a dichotomised absolute. Adopting an actor-oriented approach, Karakok ( 2009 ) identified transfer of students’ understanding of the concepts in linear algebra to quantum physics contexts. In this paper, although we utilise cognitive theoretical frameworks for analysis of individuals’ account of transfer processes, socio-cultural perspectives, acknowledging individuals own constructions of their learning, and the importance of context, frame the study more fully.

3.1. Research Design

There are two data collection strategies employed. First, student interviews look at both the processes of transfer tasks, using a think-aloud method, and a post-task interview data is also gathered on students’ perceptions about the relationship between mathematics and science and the issues related to transfer. Second, interviews with academic teaching staff explored the relationship between mathematics and science; and factors promoting and hindering transfer.

3.2. Student Think-Aloud Study

We examined first-year university students’ transfer processes by giving them physics exam questions and getting them to think-aloud while they completed this task. Post-task interviews were conducted with questions exploring student perspectives of mathematics and science learning and transfer between these.

3.2.1. Sample

Ten students in STEM degrees at an Australian university agreed to participate in this study. We purposively selected student cohorts studying first semester mathematics service courses and second semester physics courses and recruited volunteers from classes. Relevant background information was collected: age (mode = 21, range 19–32), gender (female 30%), and degree (all were Bachelor of Science, three in advanced courses and two in degrees combined with arts or education).

3.2.2. Data Collection

Individual interviews were conducted to collect the following information. First, a short half-page questionnaire was used to collect the background information of students. Second, a cognitive interview was conducted using the think-aloud method to examine the learning processes of transfer; this is explained in detail in the following sections. A third element was a post-task interview used to identify the strategies used and difficulties faced by students in the transfer task.

3.2.3. Think-Aloud Tasks

Students were asked to speak aloud about what they were thinking, while solving two physics questions extracted from first year second semester past exams (see Figure 2 ). These physics questions required mathematical skill, knowledge, and reasoning learned in mathematics service courses. The first question involved calculation of a partial derivative. The second question could be mathematically answered by solving Schrödinger equation; however, the application of mathematics to solve the second question was not obvious and an intuitive approach to physics could be employed as an alternative. To extract more insight into the students’ thinking, a follow-up interview was also used to retrospectively explore how the question was attempted.

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Think-aloud physics Questions 1 and 2 used for examination of transfer processes.

3.2.4. Data Analysis

In order to look at the processes of transfer, the six categories in Bloom’s taxonomy (see Table 1 ) and the four levels of processes of mathematical problem-solving (based on Seo 2010 , see Figure 1 ) were used as descriptive categories for the various processes students described in their think-aloud accounts. Drawing on both the think-aloud transcripts and the working out evident on the transfer task answer sheets, each process was also coded by the language used (words and phrases), mathematical expressions, formulae, or graphs (see some examples in Table 2 ). In addition, qualitative thematic analysis was used to identify themes generated from the post-task interview data.

Examples of words, phrases, and mathematical expression associated with coding four processes in solving transfer questions 1 and 2.

3.3. Interviews with Academics

An interview survey was conducted to ascertain academic practitioners’ views on teaching and learning. The academics were experts in their own research fields, and experienced in higher education teaching and learning. Almost all of them held post-graduate qualifications in higher education teaching and learning and they were able to provide informed and articulate comment on learning issues and challenges. However, it needs to be acknowledged and emphasized that whilst they provided a range of authentic practitioner perspectives, none were expert on transfer of learning. Academic teaching staff had been involved in, and interviewed, previous teaching and learning research, but not research on transfer of mathematical learning. This study addresses that gap with a preliminary, small sample.

3.3.1. Sample

The sample included eight senior teaching academics across four disciplines (5 mathematics, 1 IT, 1 physics, and 1 bioscience; 4 males and 4 females) who were invited to participate in this study and were required to meet the three conditions: (i) knowledge and experience with the issues under investigation; (ii) capacity and willingness to participate; (iii) sufficient time to participate in the interviews.

3.3.2. Data Collection and Analysis Methods

There were two rounds of interviews asking experts fifteen open-ended questions in total. In the first round, sample questions covered “What mathematical knowledge and skills taught in first year mathematics do you think are most relevant to study in biology, biochemistry, engineering and physics?” and “What factors enhance or hinder students’ application of mathematical skills and knowledge in biology, biochemistry, engineering and physics?” On the basis of analysis of responses, a second round of questions were made for clarification. For their convenience, participants were invited to answer these questions by e-mail. This email interview technique has been used in other studies on teaching and learning mathematics ( Petocz et al. 2006 ). The interview data transcribed was analysed with using thematic analysis, according to the principles outlined by Braun and Clarke ( 2006 ).

4. Results and Discussion

We present our findings and discussion around the four research questions.

4.1. Research Question 1: What Are the Processes of Transfer (If Any) Evident in Students “Think-Aloud” Accounts While Solving Physics Exam Questions Requiring Knowledge and Understanding from Their Mathematics Service Courses?

For each student, their progression through the elements of Seo’s theory (2010) interpretation, integration, planning, and execution was coded and presented in a temporal model (see Figure 3 a for question 1(a) and in Figure 3 b for question 1(b). The coding of the processes was based on categorisation of phrases and synonyms relating to each of Seo’s four processes. For example, the students’ think-aloud account was categorised as interpretation if they reported “I’m reading the question and I’m thinking about the relationship between frequency and wavelength” (Student 1) or planning if they stated “we just need to replace in the formula where the wavelength is used, … and … to multiply it by dλ df” (Student 2).

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( a ) Think-aloud reports on transfer processes for question 1. ( b ) Think-aloud reports on transfer processes for question 2.

To interpret Figure 3 a,b, the reader can view each students’ progression, and/or recursive movement, from left to right; this reflects their reported thinking over time as they attempted to answer the physics question. Comments provide additional important information and the transfer column highlights whether any transfer of mathematics learning to the physics task was evident. All students followed the interpretation, integration, planning, and execution processes highlighted in Seo’s model. But, there was one small exception, student 4, who missed the integration process. Further, some students (e.g., 5) made errors at various points (see dotted line boxes) and others still (e.g., 1) took recursive steps to modify their approach to the problem when they made initial errors.

The extent to which transfer was demonstrated varied among students and this may be related to question difficulty. In the sub-question 1(a), five students out of ten got correct answers, therefore demonstrating transfer, while in the sub-question 1(b) only three students managed this. The small proportion demonstrating transfer in the latter question was not surprising given the increased complexity of the calculation. Two students in the former and three students in the latter also were able to demonstrate understanding related to transfer to some extent; socio-cultural theories of transfer suggest this can be considered partial transfer.

This variation in how transfer was demonstrated among students is consistent with socio-cultural studies on transfer, in particular the actor-oriented approach to transfer (see for example, Karakok 2009 ), which highlights diversity in how transfer of learning is constructed within individuals learning. Furthermore, analysis of the think-aloud accounts made it evident that metacognition was also an important part in all four of the transfer processes. For example, students reviewed their planning and calculation, some of them more than once, and this reflects their conscious monitoring of their own thinking.

There were students who couldn’t solve the questions. For example, some students had the right approach, but couldn’t fully demonstrate transfer (see cases 6 and 7 in Figure 3 b). It is important to consider what made the questions difficult for these students. There were five main issues identified from the analysis of the transfer processes: (i) lack of mathematical knowledge, related to the first and foundational category of Bloom’s taxonomy—knowledge; (ii) difficulties in understanding the question, i.e., issues in translation and integration processes, related to the second category of Bloom’s Taxonomy—comprehension (iii) issues in recalling prior learning, which are also related to the first category of Bloom’s taxonomy as well as planning and execution processes; (iv) a lack of procedural knowledge to solve the question—or poor use of problem schema; (v) a lack of practice and/or a technical error in calculation, related to the execution process. In the post-task interviews, students mentioned difficulties in understanding the problem, for example:

“in the first question with the Planck’s formula, it’s difficult to understand the question to begin with. So reading through the question is a lot to figure out. And you also have to recognise lots of different symbols, maths symbols which I’m sure if I didn’t know what they were I would be very lost, even more than I was.”

In regard to problem schema, another student also stated that “I understand what it’s asking me to do. I just don’t know how to do it.” These issues, mentioned by students, are consistent with the difficulties in mathematical learning outlined by Seo ( 2010 ). These difficulties are orientated to the nature of the question task as questions 1(a) and (b) relied mostly on calculation. However, as we will explain, these contrasted substantially with difficulties in Question 2, which had much greater demands on reasoning abilities.

Question 2 (see Figure 2 ), required thinking about probability distributions and exponential decay, however, no students were able to solve this question using mathematical formula in physics or mathematical reasoning in an explicit way. This led to difficulty in conducting the analysis and transfer could not be observed. For question 2, the students preferred recalling their basic knowledge of physics and tended to utilise an intuitive approach to solve the physics questio—rather than employing the mathematical methods taught in their service course in the previous semester. Although it is important for students to understand the concepts in physics in their disciplinary ways, the understanding of mathematical expressions behind the physical world is equally important and of perhaps greater utility in terms of development of their generic 21C skills.

The students’ inability to approach Question 2 with mathematical understanding may be related to a disparity between mathematical service courses and physics courses, wherein the content taught in mathematics classes is assumed and not reinforced within the physics classes. In other words, conceptual understanding in physics may be overemphasised or presented without in-depth exploration and reinforcement of the relevant mathematical aspects. This issue was touched on by one academic who suggested in interview: “I suppose there is so much to test conceptually in the sciences in an examination that educators do not want students to spend time on mathematical working.”

It is impossible to verify if this was the case. We can only report that for Question 2, the analysis of transfer of mathematical learning was not possible because mathematical reasoning was not evident in the students’ think-aloud accounts.

4.2. Research Question 2: What Are the Challenges in Transfer of Learning Reported by Students and by Academics?

Interviews explored students’ and academics’ perspectives on what would hinder and enhance transfer of learning. This was done through a series of open-ended questions which generated a large amount of data. After coding students and academics responses separately and producing highly synthesised themes, we compare and contrast these in Table 3 .

Potential factors promoting or hindering transfer.

The factors reported here were consistent with a range of educational research studies and theories. For example, Hattie’s ( 2009 ) syntheses of meta-analyses in education also highlights teacher clarity and feedback as among the most influential teaching factors. Unsurprisingly, mathematical anxiety was mentioned by both students and academics. Academics are aware of their students’ mathematics anxiety: “I imagine this is to do with a perceived lack of mathematical understanding, and fear of mathematics, among students (and society in general)” (Academic 2). Both students’ and academics’ comments on mathematics anxiety made it clear that this was an issue central to teaching and learning. We wondered if students’ fear of mathematics and academics’ sensitivity to this fear was related to the low level of science academics’ inclusion of mathematics in science assessments. We reviewed a range of science exam papers in the university and found there were no questions aligning with the tertiary mathematics taught in mathematics service courses in either biology or bio-chemistry, despite the fact that students were required to undertake those university mathematics courses. In physics, just a few questions were aligned with the university mathematics service courses, with high-school level mathematics (advanced and extension courses) more evident in the first-year physics papers. If mathematical learning is not assessed in the science disciplinary context, why are mathematics courses compulsory for science students at university? Unfortunately, in the interviews academics provided no direct replies to this question. What was evident is that first, both students and academics acknowledge the importance of mathematics, but academics think that understanding concepts in their disciplines is more important than mathematical application. Second, scientist academics think that mathematical skills and understanding should be assessed in mathematics service courses, not science courses. Academics also acknowledged that it is very problematic and difficult to provide mathematics courses to accommodate learning needs for students from diverse backgrounds and disciplines, such as science and engineering.

Finally, “translation” emerged as an important theme for academics (see Table 3 ), and although students did not mention it in interview, it did emerge as an issue for students in the think-aloud task. Translation was seen as important as science problems with mathematical content can be expressed in the form of word problems. One academic commented: “the most difficult problem for many students is not with the maths but converting from words to maths and back” (Academic 3). Our analysis of the transfer processes showed translation was an important strategy for successful transfer of learning and problem-solving (see student 1 in Figure 3 a).

4.3. Research Question 3: What Teaching and Learning Factors Do Students and Academics Believe to Enhance Transfer?

Interviews also explored students and academic perspectives on what would enhance transfer of learning, shown in Table 3 . Overall, the reported factors are consistent with a range of educational theories; for example, transfer theory suggests that higher order thinking, like application of understanding in real world problems and rehearsal (repeated practice) are enablers of transfer. Furthermore, students’ self-beliefs and confidence are important in learning in general ( Malmberg et al. 2013 ); as is prior learning ( Martin et al. 2013 ). Thus early experiences in mathematical learning are likely to be a key to successful transfer of mathematical understanding in university.

There was one noticeable gap between student and academic perspectives. While academics expect students to: “try to see wider connections and the historical development of mathematics in science, rather than only focus on a narrow disciplinary context” (Academic 4); the student perspective suggests that in relation to science learning “the basics of maths that I’ve done [are] very useful, but the stuff I’m doing right now is not quite so” (Student 10). In interviews, academics identified the relevance of mathematics and interdisciplinary learning as important; by contrast, students did not mention these as important to promoting transfer ( Table 3 ). This demonstrates an apparent gap in how the utility of mathematics is perceived by students and academics. Socio-cultural theories of transfer suggest that students need to value learning in order to transfer it effectively. If academics are not able to communicate the value and potential of the mathematics learning, this presents a barrier to transfer.

There are factors identified in research as important to transfer, which were not apparent in these academics’ and students’ responses. First, although socio-cultural theories emphasise the importance of learning contexts, none of the interview respondents mentioned contextual factors like interaction with lecturers, tutors and/or peers in lectures, tutorials, or other situations and material available for learning.

Also unexpectedly, neither students nor academics mentioned metacognition in the interviews. This is despite the fact that research literature views metacognitive knowledge and activities, such as monitoring and control, as essential to mathematical learning and transfer ( Billing 2007 ; Mayer and Wittrock 1996 ; Okamoto 2008 ; Seo 2010 ). It was evident in think-aloud that students employed metacognitive strategies; for example, when monitoring calculation a student stated “Did I do something wrong?” (Student 3). However, they may not have been aware of this strategy and did not discuss this in the post-task interviews. As education systems shift to focus on transferability of generic skills, they are likely to promote a stronger focus on metacognitive skills.

5. Conclusions

Although it is ubiquitous to all conceptions of learning, “transfer of learning” is in keener focus within the modern conception of 21C skills due to their aims to provide generic skills and competencies. Such skills need to be carried and applied, or transferred, to a wide range of contexts. It is anticipated that the demands of future learning, within rapidly changing environments, will require increasing competence in transfer of learning. Our experience exploring “transfer of learning” between mathematics and science at one university has highlighted a range of issues and possibilities.

Firstly, through this study, we were able to demonstrate how a process-oriented approach ( Sternberg 2000 ) to learning could be applied in an authentic educational context, documenting student thinking during a mathematical problem-solving transfer task. Although small in scale, the findings demonstrate how a larger study using this approach could be used to provide diagnostic information to strengthen teaching and learning. Using Seo’s problem-solving theory and Bloom’s taxonomy, students’ thinking pathways and stumbling blocks in this process could be analysed by teacher academics. Common difficulties can be identified and teaching and learning designed to rectify them. While diagnostic assessments for school and adult literacy and numeracy have been available for decades, there remains potential to develop similar methods for practitioners teaching for 21C skills, including transfer of learning. There are already a range of assessments of problem-solving skills that might be adapted to this purpose.

Our findings provide the opportunity to reflect on models for the teaching of mathematics in universities. We argue that transfer of learning between mathematics and science is a neglected area of enormous potential. Given societal demands for increasing STEM capacity, it is critical and inevitable that we reflect on the following questions: What are the key challenges in mathematics learning for sciences? How should mathematics be taught to university science students? Transfer of learning, and associated cognitive and socio-cultural theories, provide a conceptual framework through which these questions can be considered and explored.

The close relationship between mathematics and intellgience factors, the demands for numeracy and data skills from industry, and the potential of mathematics in problem-solving all suggest that reforms in mathematics education are needed and should be discssed as part of the 21C skills agenda. There are currently different models for higher education mathematics across countries and institutions. For some countries, such as Australia, mathematics service courses are provided, particularly for first year undergraduate students. These aim to enable students to apply mathematics to diverse contexts, but might make it difficult for students to see the connection with their own discipline. In other countries, such as Japan, it is more common that each discipline teaches mathematics, and courses are embedded into each discipline’s degree programs. This may help students to apply mathematics to their own disciplinary context, but may restrict the applicability of mathematics to more diverse contexts. Investigations into the effectiveness of each approach would be useful, as would broader contemplation of how mathematics might be positioned within interdisciplinary learning and strategy for developing 21C skills.

Finally, this project has led us to believe that greater attention could, and should, be paid to the concept of “transfer of learning” in order to promote 21C skills. While there is a long literature on transfer, there are enormous gaps in relation to how transfer might be measured, evaluated, and promoted within schools and universities. Now is the time to bring what we know of transfer into the realm of educational practice; and where understanding is lacking we must develop a program of research. In particular, interdisciplinary learning, which has been positioned as a key goal for the future (as technology and industry developments are expected to morph and transform traditional disciplinary boundaries) has not been thoroughly explored at the level of institutional practice.

While 21C skills lauded as critical for the new century include problem-solving skills, and recent educational rhetoric continues to exhort the value of integrated interdisciplinary learning, particularly in STEM, there remains a need for research examining if, and how, pursuit of these goals is evident in current educational practice.

Author Contributions

Conceptualisation, Y.N.; methodology, Y.N. and R.W.; formal analysis, Y.N; writing—original draft, Y.N.; writing—review & editing, R.W. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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interdisciplinary problem solving

Why solving the world’s problems needs to start a multi-disciplinary approach

Why solving the world's problems needs to start a multi-disciplinary approach

Labour Day is our New Year’s Eve. Rather than vowing to lose weight or spend less time on our phones, as college professors we head into the new school year with a different kind of resolution: to inspire and prepare our students to become agents of positive change.

The world’s problems certainly didn’t take a break this summer, and we know that successfully addressing them depends on a mindset much broader than any one discipline can offer. Our strategy is to cultivate a way of thinking that blends insights from multiple perspectives.

As a psychologist , an anthropologist and a  historian who teach at an engineering college, happily, we see examples of this kind of integration all around us.

Global climate change may be the biggest challenge facing humanity, and it is a problem that illustrates the world-changing implications of interdisciplinary problem-solving. In an analysis of the economic impact of reducing greenhouse gas emissions, experts at the consulting firm McKinsey & Company identified a spectrum of strategies and their associated costs.

Want to solve the world’s problems? Try working together across disciplines https://t.co/hsyoFJ4dy2 pic.twitter.com/0osutHaKsr — The Conversation Ed (@TC_education) September 2, 2018

Options like converting to nuclear energy, shifting to electric vehicles, and retrofitting coal and gas plants all have great potential, but we can produce the most benefits for the lowest cost by adopting strategies such as switching homes to energy efficient lighting and better insulating our residences and workplaces.

Compared to changing the national energy supply chain, these changes aren’t highly technical. They are matters of changing human beliefs and behavior.

An article published in Science last year diagnosed the real problem of climate change in this way: “Experiencing the self as separate from nature is the foundation of humanity’s damaged relationship to planetary resources.”

The only real solution to the climate problems facing our planet is to change mindsets, an approach that requires a complex understanding of all the ways that individuals and institutions interact with the natural world.

In other words, students should not only study the social sciences or the natural sciences, but also learn how the insights gained from both can be combined to be even more powerful.

The importance of making connections across perspectives also plays out at the local level.

One traffic intersection in the center of Drachten, Netherlands, accommodates 20,000 drivers as well as many bicyclists and pedestrians each day. As a result, it became notorious for its high rate of accidents and deaths.

A conventional solution might have been to load up the roads with signage and signals that clearly instruct everyone where to go and when. But when Dutch traffic engineer Hans Monderman approached the problem, he saw the congested conduit as a place of profound disconnection. Rather than peppering the roads with signs, in 2003 he took all signage away.

This approach to “shared space” design meant that drivers, cyclists and pedestrians had to increase their awareness of each other to successfully navigate the intersection. This reliance on human connection rather than engineered traffic patterns upended conventional thinking, and dramatically decreased the number of accidents and deaths.

The most innovative solutions to local problems like this demand deep integration of quantitative and emotional insights that are too often segregated between traditional academic disciplines.

interdisciplinary problem solving

We owe Drachten’s traffic success to multi-disciplinary thinking. Source: Shutterstock

Individually

Finally, we see many challenges at the individual, personal level that call out for integrated thinking.

Terri, a Boston-area woman in her 60s who uses a wheelchair, told a team in one of our engineering design classes here at Olin College of Engineering that she finds grocery shopping a cumbersome and physically painful experience. A traditional engineer’s answer might point her to online services that could provide convenient in-home grocery delivery without unpleasant exertion.

But when our students joined Terri at the supermarket, tried to navigate the store from her wheelchair, and spent time with her in her home, they discovered something unexpected.

For Terri, grocery shopping wasn’t only focused on getting food, but offered a special opportunity to laugh with the butcher, exercise autonomy and experience community membership. An online service could deliver her ground turkey, but it would also make her feel lonely.

The students’ solution was a custom easily adaptable rack for the chair – painted bright purple, Terri’s favorite color – that eased the physical challenges of shopping while enhancing her ability to engage with her community in a meaningful way.

Devising this solution required a nimble synthesis of engineering design and attention to human values.

Teaching new approaches

As these examples illustrate, we need to teach students to approach complex problems differently. Our future is at stake.

This past May, a joint task force from the National Academies of Sciences, Engineering, and Medicine released a report entitled, Branches From the Same Tree: The Integration of the Humanities and Arts with Sciences, Engineering, and Medicine in Higher Education .

interdisciplinary problem solving

Devising real solutions starts with combining STEM with human values. Source: Shutterstock

This study identified the great potential in interdisciplinary education. The list of possible benefits include improved student motivation and enjoyment of learning, development of teamwork and communication skills, ethical decision-making and critical thinking.

Done correctly, engineering begins and ends with people. Done optimally, tackling our world’s biggest challenges requires a diverse and integrative approach.

We see encouraging examples of this type of innovative integration in diverse corners of academia. For example, at George Mason University, the Rain Project, part of the EcoScience + Art Initiative brought together faculty from sciences, arts, humanities and design departments to develop a floating wetland.

The project not only improved water quality and stormwater management, but also demonstrated the local community’s dependence on their wetlands for survival. Or the STAGE Lab at the University of Chicago, where new pieces of theater and film are created within the context of the Institute for Molecular Engineering.

Here, the creation of new plays and films alongside the creation of new scientific findings inspires new ways of asking questions, in both art and science.

Ethics, sustainability, questions of identity, equity or social justice, and many other topics, must be included in the scientist’s or engineer’s design process.

And their repertoire must include rigorous communication, teaming, self-directed learning, self-reflection and other skills. Similarly, artists, writers, managers and other non-technical professionals lose out when their work ends where scientific thinking begins.

Our Labour Day resolution this year won’t help us with weight or time management. Instead, it will help us to humbly remember the limits of any one way of thinking about major challenges and the promise of true integration.

By  Jonathan M Adler , Associate Professor of Psychology, Olin College of Engineering ; Caitrin Lynch , Professor of Anthropology, Olin College of Engineering , and Robert Martello , Professor of the History of Science and Technology, Olin College of Engineering

This article was originally published on The Conversation . Read the original article .

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VentureWell

the what, why, and how of a multi-disciplinary approach to problem-solving

problem-solving

From student teams to scientific and academic institutions, many recognize that complex problems are best solved when a group leverages diverse perspectives, expanding the possible solutions through multiple business, technology, design, social science, and scientific inputs. As we’ve been paying close attention to trends related to multi-disciplinary problem-solving in our field, we asked ourselves: which stakeholders should be at the table when addressing big problems? And what role should faculty play in these efforts? 

To help answer these questions, we’ve sought out others in the innovation and entrepreneurship community collaborating at the intersection of diverse disciplines and sectors to solve grand challenges. Jonathan Fay is the executive director at the Center for Entrepreneurship at the University of Michigan as well as the executive director for the Midwest I-Corps Node. Fay led a panel discussion during OPEN 2019 on how faculty at the University of Michigan are bringing together engineering, law, business, policy, and environmental students with industry to find opportunities to develop high-value products from captured CO2. 

Dorn Carranza , director of innovation and industrial partnerships at VentureWell, spoke with Fay at OPEN about the what, why, and how of convening key stakeholders to solve big problems. Here’s an excerpt of their conversation. 

Dorn Carranza: What groups should be represented when taking a multi-disciplinary approach to problem-solving?   

Jonathan Fay: I always think of multi-disciplinary teams as a four-legged stool. You need universities, industry, startups, and government entities all working together to make something happen. Take climate change —a massive issue. We need to take gigatons of carbon dioxide out of the atmosphere. A startup trying to do that alone? Good luck. 

But as a collaborative effort, much more can be accomplished. For example, we need the startups to develop the ideas and new approaches to a solution. They can then partner with bigger companies to scale those ideas. Universities contribute research and talent to the mix. Since many of these projects aren’t viable business ideas at the beginning, government plays a role by supporting these projects with funding or other resources to take them to the next level.  

Carranza: How are activities coordinated among these stakeholders?

Fay: Coordinating different players is challenging. That’s why the problem’s scope needs to be very clear at the outset. If you’re too vague, the stakeholders are not sure exactly why they’re there, but if you go narrow enough, then they’re willing to participate. You can see their level of interest in solving that problem, and that’s what you want—people who are going to put real time and money behind solving a problem that’s meaningful to them. That’s also a good Litmus test to see if you’ve got the right people around the table.

Carranza: Who do you recommend start this process? 

Fay: I think universities are best positioned to start this process. It would be difficult for a big company to start it, because they’ve got competitive pressures and other logistical challenges. Startups often don’t have the bandwidth to do it, but they’d be happy to jump on and join other like-minded people that can help them. Government is constrained in what they can do on many levels. If a university president feels very strongly about something, they’ll support a faculty researcher or tech transfer office to lead the coordination process. Or you can find a dean or provost to support your efforts.

Carranza: What programs are you running at the entrepreneurship center that bring different disciplines together to solve big problems?

Fay: The College of Engineering as a whole just launched something called the Blue Sky Initiative , which are internal awards—up to two million per year—to bring faculty together to solve big problems. The one that our entrepreneurship center is most deeply involved with is something called the CO2 Global Initiative. We have researchers from the environmental sciences, engineering, and social sciences looking at solving this problem of climate change and the CO2 build-up in the atmosphere.

We’re also bringing together a diverse group of students that then can work with the researchers to figure out how to commercialize a discovery and actually have an impact on the world. The researchers are heads down doing the science. They don’t have the bandwidth or even inclination to take on commercialization. We’ve got some really great students from the business school, the engineering school, and the environmental sciences schools to collaborate in that effort.

Carranza: How are you engaging faculty to get involved in projects that are bringing all these different disciplines and stakeholders together?

Fay: As I mentioned, faculty contribute most of their effort on the research side. For example, one researcher is working on a new way of capturing carbon from the atmosphere, and he’s able to increase the absorbance of a certain chemical to CO2 by a factor of two. My question is: is that enough? Should it be ten? Should it be a thousand? What moves the needles? That’s where faculty can get students involved to help answer some of these questions.

interdisciplinary problem solving

Learn more about our work with the NSF Industry-University Cooperative Research Centers (IUCRC).

Photo credit: Blue Sky Initiative/Michigan Engineering

  • I&E ecosystems

Interdisciplinary problem- solving: emerging modes in integrative systems biology

  • Original Paper in Philosophy of Biology
  • Published: 20 July 2016
  • Volume 6 , pages 401–418, ( 2016 )

Cite this article

  • Miles MacLeod 1 &
  • Nancy J. Nersessian 2  

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Integrative systems biology is an emerging field that attempts to integrate computation, applied mathematics, engineering concepts and methods, and biological experimentation in order to model large-scale complex biochemical networks. The field is thus an important contemporary instance of an interdisciplinary approach to solving complex problems. Interdisciplinary science is a recent topic in the philosophy of science. Determining what is philosophically important and distinct about interdisciplinary practices requires detailed accounts of problem-solving practices that attempt to understand how specific practices address the challenges and constraints of interdisciplinary research in different contexts. In this paper we draw from our 5-year empirical ethnographic study of two systems biology labs and their collaborations with experimental biologists to analyze a significant problem-solving approach in ISB, which we call adaptive problem solving . ISB lacks much of the methodological and theoretical resources usually found in disciplines in the natural sciences, such as methodological frameworks that prescribe reliable model-building processes. Researchers in our labs compensate for the lack of these and for the complexity of their problems by using a range of heuristics and experimenting with multiple methods and concepts from the background fields available to them. Using these resources researchers search out good techniques and practices for transforming intractable problems into potentially solvable ones. The relative freedom lab directors grant their researchers to explore methodological options and find good practices that suit their problems is not only a response to the complex interdisciplinary nature of the specific problem, but also provides the field itself with an opportunity to discover more general methodological approaches and develop theories of biological systems. Such developments in turn can help to establish the field as an identifiably distinct and successful approach to understanding biological systems.

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As O’Malley and Dupré point out however there is not a well-developed concept of “system” that all groups and sub-groups share ( 2005 ). Further many systems biologists are rather ambivalent towards pursuing a general theory of biological systems and acknowledge that a sufficient class of well-validated and robust models does not yet exist. They describe the current shared commitment of systems biologists as a commitment towards an approach that “foregrounds mathematical modeling in order to transcend piecemeal analysis.” (1273)

This research was funded by the US National Science Foundation.

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Acknowledgments

We appreciate the support of the US National Science Foundation in conducting this research (DRL097394084). Miles MacLeod’s participation was also supported by a postdoctoral fellowship at the Academy of Finland Centre of Excellence in the Philosophy of the Social Sciences, University of Helsinki. Members of the Centre contributed much advice in earlier developments of this paper. We thank the directors of Lab C and Lab G for welcoming us into the lab and the lab-members of those labs for granting us numerous interviews. We thank the members of our research group for contributing valuable insights, especially Vrishali Subramanhian, Lisa Osbeck, Sanjay Chandrasekharan, and Wendy Newstetter. We also thank the three anonymous reviewers whose comments substantially improved the paper.

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MacLeod, M., Nersessian, N.J. Interdisciplinary problem- solving: emerging modes in integrative systems biology. Euro Jnl Phil Sci 6 , 401–418 (2016). https://doi.org/10.1007/s13194-016-0157-x

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Published : 20 July 2016

Issue Date : October 2016

DOI : https://doi.org/10.1007/s13194-016-0157-x

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Interdisciplinary problem solving.

Nov 3, 2021

interdisciplinary problem solving

UD’s Arthi Jayaraman is the Centennial Term Professor in the Department of Chemical and Biomolecular Engineering with a joint appointment in Department of Materials Science and Engineering. Both departments are within the College of Engineering.

Computing, engineering and polymer sciences converge in new NSF doctoral traineeship

Big-name chemical companies like DuPont and W.L. Gore have complex materials problems to solve. The trouble is they’re in need of well-rounded researchers to find the solutions they’ve been looking for, ideally highly skilled scientists with more than one area of expertise—like someone fluent in both materials engineering and computer science.

Recognizing that real-world need,  award-winning UD Professor Arthi Jayaraman  has created a collaborative, cross-disciplinary traineeship that will provide selected doctoral students from the University of Delaware and Delaware State University with the technical and professional training they need to thrive in their careers after graduation.

interdisciplinary problem solving

Anshuman Razdan

“That’s part of our mission, it’s at the core of what we do: Prepare our students, whether it’s for a life after as faculty or in national laboratories or industry,” said Anshuman (“A.R.”) Razdan, associate vice president of research development in UD’s Research Office. Jayaraman credited Razdan, along with Graduate College Dean Louis Rossi, for playing key roles in bringing her idea for this traineeship program to life.

“This is not a Ph.D. program by itself, but is designed to make the graduate student experience better,” Razdan said. “It’s an interdisciplinary collision, in a positive sense, and builds on extensive UD investment and success in the data sciences.”

The new National Science Foundation-funded Research Traineeship “Computing and Data Science Training for Materials Innovation, Discovery, AnalyticS” (NRT-MIDAS) will teach doctoral students in computer and information sciences, electrical and computing engineering, chemical engineering, materials science and engineering, biomedical engineering and chemistry programs how to use high-performance computing and data science to lead to new discoveries and innovations in the field of polymers.

NSF has awarded Jayaraman a nearly $3 million grant to support this traineeship over the next five years. Jayaraman, Centennial Term Professor in UD’s College of Engineering’s department of Chemical and Biomolecular Engineering with a joint appointment in Materials Science and Engineering, will serve as director of this traineeship program. This traineeship will work with 50 to 100 UD and DSU doctoral students, some of whom will receive financial support for two years through this NSF grant. International students will also be able to apply to the traineeship program and some selected students may receive one semester of financial support from the College of Engineering.

The program is slated to admit its first cohort of new UD and Delaware State University graduate students from one of the six specified programs in winter 2022.  Applications  are due by Tuesday, Nov. 30, and selections will be made by Wednesday, Dec. 15.

Besides the interdisciplinary technical skills, trainees will also learn the essential professional skills that every employer wants to see in their employees: Researchers who know how to interact with team members from diverse backgrounds and know the importance of adaptable science communication both in the laboratory and to the broader community.

“All of the training elements were strategically selected: The technical training elements, applying computing and data science to polymer problems in the real world, combined with professional training elements where trainees work in teams with people who aren’t from the same discipline, learning to communicate, and going above and beyond to explain their work to the other person,” Jayaraman said. “Essentially this MIDAS traineeship is that extra, customized, all-rounded training layer we’re putting on top of what these doctoral students receive in their own graduate programs.”

interdisciplinary problem solving

In this photo taken before the coronavirus pandemic necessitated the wearing of masks and distancing in classrooms, Prof. Arthi Jayaraman speaks with students in her chemical engineering class.

The diverse NRT core faculty team facilitating this collaborative training environment were also strategically selected, and were chosen because of their accomplishments and expertise in one or two of the relevant disciplines. For example, Prof. Laure Kayser with the Department of Materials Science and Engineering has expertise in polymer materials for organic electronics, Prof. Austin Brockmeier with the Data Science Institute and the Department of Electrical and Computer Engineering has expertise in data science applied to a variety of domain sciences, while Prof. Sunita Chandrasekaran with the Department of Computer and Information Sciences brings her expertise in high-performance computing.

On the forefront of solutions

Since polymers are used in everything from food packaging and paints to electronics and medical settings, companies are constantly searching for the latest and greatest materials for, say, an airplane body or COVID-19 vaccine delivery. That means both industry and academia are often pursuing ways to optimize polymers, turning to chemistry, materials science and engineering for solutions.

By offering professional cross-training in those disciplines as well as computer science and data science, Jayaraman hopes trainees will learn how to let the machines handle the optimization and avoid the tedious trial and error that would usually come with running all possible experiments in the lab. By combining disciplines, they can use computing, modeling and artificial intelligence to save the chemicals, time and effort that extensive laboratory experiments typically need.

“If you just did experiments in a lab, you’d test one chemical and ask, ‘How does it perform? How does it behave?’ and then move to the next chemical and repeat the process. This is trial and error,” Jayaraman said. “Companies often want to find faster and cheaper ways to explore different chemicals and get to the better-performing product.”

interdisciplinary problem solving

Joshua Enszer

That’s why Jayaraman made sure the program is partnering with companies searching for such solutions. In addition to DuPont and W.L. Gore, the traineeship has also established industry partnerships with Argonne National Laboratory, Brookhaven National Laboratory, Merck & Co. and Procter & Gamble, with more companies expected to join in the coming months and years.

An “NRT-Hackathon” course that is being designed for the traineeship program, after trainees complete core classes and right before they explore internships, will collect real-world problems from participating companies and turn them over to small teams of students to explore and solve with computing and data science tools over the course of a semester.

“Each problem will be a semester-long problem, and students from different disciplines in each team will have to teach each other what it means,” Jayaraman said, noting that a computer sciences student will need to learn the specific properties of a chemical, while the chemical engineer sharing that information will have to learn about the computing methods their computer science colleague is using to develop data-based solutions.

Not only will the training benefit students, but it will also serve existing and future industries by preparing a well-rounded workforce and also finding ways to solve real problems by replacing trial-and-error based experiments with computing-based approaches.

“It more than bridges the gap, it has a serious economic impact,” Razdan said, noting that the program could also help participating students decide whether a life in industry or academia is better for them.

In addition to these custom interdisciplinary courses that emphasize the importance of clear communication across disciplines, trainees will also complete their regular graduate work, and benefit from a secondary NRT-MIDAS-specific adviser.

“The way a chemical engineer talks and the way a computer scientist talks is not the same,” Jayaraman said. “We want to sharpen those professional skills, especially cross-disciplinary communications, by working in team environments with different backgrounds, both culturally and technically.”

An academic approach

Not all of the talented graduate students that will be selected for this traineeship will pursue industry careers; some may want to work in academia, where they could foster this comprehensive approach in their own future classrooms. Those pedagogically minded students will hone the teaching and communications skills they’ll need, but would otherwise not be included in their normal graduate programs. With this motivation, Jayaraman recruited a pedagogical expert into the NRT-MIDAS core faculty team.

interdisciplinary problem solving

“What we’re trying to do here is fill in a big need to have people who are better teachers from the start,” said Joshua Enszer, a chemical and biomolecular engineering associate professor and member of the NRT core faculty team. The NRT-MIDAS teaching fellowship builds off a program underway in the Department of Chemical and Biomolecular Engineering, where a handful of fellows work with faculty to actually implement a course during their graduate studies, he said.

“Because we’re bringing together these very important and very related areas, we’re working on helping improve communication on both sides,” Enszer said. “Bringing that together and then teaching everyone together is a really exciting opportunity. I think it’s going to help prepare this generation of graduate students for a variety of potential different careers.”

A diverse NRT-MIDAS core faculty team of nine faculty members, including Jayaraman, will provide technical and research training and mentoring. An independent advisory council, made up of six international experts from academia, national laboratories and industry, will offer their perspectives and recommendations in order to strengthen this interdisciplinary traineeship.

“UD really is an excellent environment for doing team science,” said Prof. Cathy Wu, an NRT-MIDAS core faculty member and Unidel Edward G. Jefferson Chair in Engineering and Computer Science, director of the Center for Bioinformatics and Computational Biology, director of the Data Science Institute and director of the Protein Information Resource. “This is just a great example of how Arthi (Jayaraman) has brought such an excellent, diverse team together for this particular training grant. But if we look around UD, this kind of very collaborative effort is happening with many different initiatives. I think team science, this kind of very inclusive environment, really is a signature of what we do at UD.”

Jayaraman and others at UD hope this training continues beyond the recently awarded grant.

“I tell faculty it’s like building a building,” Razdan said. “We want the faculty focused on building the building, constructing the idea. All of us, we’re here to support Arthi with scaffolding so she has everything she needs to imagine and execute the ideas that can only come from her. We’re very, very happy to be that scaffolding for her.”

Article by Maddy Lauria |   Photos by Evan Krape |   November 02, 2021

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