Linking learning and research through transdisciplinary competences

Community member post by BinBin Pearce

BinBin Pearce (biography)

What are the objectives of transdisciplinary learning? What are the key competences and how do they relate to both educational goals and transdisciplinary research goals? At Transdisciplinarity Lab (TdLab), our group answered these questions by observing and reflecting upon the six courses at Bachelor’s, Master’s, and PhD levels that we design and teach in the Department of Environmental Systems Science at ETH Zurich, Switzerland.

Six competence fields describe what we hope students can do with the help of our courses. A competence field contains a set of interconnected learning objectives for students. We use these competence fields as the basis for curriculum design. The competence fields were identified by reflecting on actual skills needed to conduct a transdisciplinary research process and by identifying elements from courses that have proven to be meaningful for students personally.

These competence fields are:

  1. Communicating values – Students are able to identify, ground and communicate assumptions and normative values in topics related to the concept of sustainable development.
  2. Reflecting about self and others – Students are reflective about their own perceptions and biases with regards to sustainable development.
  3. Applying concepts in the real-world – Students are able to appropriately apply conceptual knowledge to specific contexts, and, in parallel, exercise practical skills (such as project organization and time management) to deliver the required end products.
  4. Framing complex problems with others – Given a real-world topic and its accompanying conflicts and uncertainties, students are able to identify and frame clear, relevant problems with those who have contrasting perspectives or opinions.
  5. Researching in and with the real-world – Students are able to translate real-world problems into viable research questions. They are also able to identify the adequate research method(s) to investigate these questions and to co-produce knowledge with society.
  6. Imagining solutions and their consequences – Students are able to explore and develop solutions for real-world problems, while being aware of the possibility of unintended consequences of these solutions and taking responsibility for these consequences.

In making the link between transdisciplinary learning and research, we overlaid these competence fields with a pedagogical taxonomy and a transdisciplinary research framework to understand how these competences might contribute to the development of the student and to a transdisciplinary research process.

The pedagogical model is the classic Bloom’s Taxonomy (1986), which classifies learning objectives according to three domains: the cognitive, affective, and psychomotor or sensory domains. The cognitive learning domain encompasses reasoning and analytical skills. The affective learning domain describes the skills to be aware of self and others in terms of attitudes, emotions and feelings. The psychomotor domain focuses on physical, mechanical and sensory skills.

The transdisciplinary research framework is a sequence of design principles for the three phases of a transdisciplinary research process, as defined by Lang and colleagues (2012), sketched out in the table below. We matched a transdisciplinary competence field to the design principle(s) that would benefit from the application of the competence. In addition, we also matched Bloom’s taxonomy learning domains to each transdisciplinary design principle. The table below reveals the connection between the three schemes.

Transdisciplinary competence fields matched with the transdisciplinary research framework and Bloom’s taxonomy (source: Pearce et al., 2018)

The implications of these connections can be summarized as follows:

  • Both transdisciplinary research and transdisciplinary learning require the development of not only cognitive skills, but also affective and psychomotor skills, which include inter- and intra-personal skills, including the ability to communicate, to reflect, and to perceive the feeling and position of others. With the need to access skills in different learning domains, transdisciplinary research and learning are activities that develop the entire capacity of human learning, rather than focusing only on cognitive skills.
  • The transdisciplinary competences cover the span of skills needed for conducting an effective transdisciplinary research process. The list of competences listed here could serve as a reasonable foundation for a transdisciplinary education.
  • Skills needed to carry out a transdisciplinary research process straddle different learning domains. This suggests, for example, that cognitive skills could be developed alongside affective skills, rather than each being developed in isolation.

We hope that this framework may serve as a starting point for the design of other courses aimed at training future transdisciplinarians. As this work is in the beginning stages, we would also love to explore some of these concepts further with you. We look forward to hearing your experiences and comments.

To find out more about this framework and our teaching concepts:
Pearce, B., Adler, C., Senn, L., Krütli, P., Stauffacher, M. and Pohl, C. (2018, fothcoming). Making the link between transdisciplinary learning and research. In, D. Fam, L. Neuhauser, P. Gibbs (eds), Transdisciplinary theory, practice and education: The art of collaborative research and collective learning. Springer: Basel, Switzerland. Online: https://www.springer.com/us/book/9783319937427

References:
Bloom, B. S. (ed.) (1986). Taxonomy of Educational Objectives. 2nd ed., Longman: New York: United States of America.

Lang, D. J., Wiek, A., Bergmann, M., Stauffacher, M., Martens, P., Moll, P., Swilling, M. and Thomas, C. (2012). Transdisciplinary research in sustainability science: Practice, principles, and challenges. Sustainability Science, 7, S1: 25–43. Online (DOI): 10.1007/s11625-011-0149-x

Biography: BinBin Pearce PhD is a lecturer, curriculum developer, and post-doctoral researcher in the Department of Environmental Systems Science at ETH Zurich in Switzerland. Her focus is on developing tools and methods that foster students’ ability to perceive and resolve complexity in the real world with clarity and creativity, by integrating design thinking and systems thinking methodologies. She is a part of the teaching team for a yearlong course for first-year Bachelor students, “Umweltproblemlösen” (Environmental Problem Solving) and for a Masters-level course called “Transdisciplinary Case Study”. She is also the coordinator and coach for the Transdisciplinarity Lab Winter School “Science meets Practice”, a week-long training program which aims to foster skills for PhD students from all disciplines to see how perspectives in research could be interpreted for societal needs.

Synthesis of knowledge about participatory modeling: How a group’s perceptions changed over time

Community member post by Rebecca Jordan

Rebecca Jordan (biography)

How do a group’s perceptions change over time, when members across a range of institutions are brought together at regular intervals to synthesize ideas? Synthesis centers have been established to catalyze more effective cross-disciplinary research on complex problems, as described in the blog post ‘Synthesis centers as critical research infrastructure‘, by Andrew Campbell.

I co-led a group synthesizing ideas about participatory modeling as one of the activities at the National Socio-Environmental Synthesis Center (SESYNC). We met in Annapolis, Maryland, USA, four times over three years for 3-4 days per meeting. Our task was to synthesize what is known about participatory modeling tools, processes, and outcomes, especially in environmental and natural resources management contexts.

The group defined participatory modeling as a “purposeful learning process for action that engages the implicit and explicit knowledge of stakeholders to create formalized and shared representation(s) of reality” (participatorymodeling.org). In its idealized form, participatory modeling involves stakeholders in co-formulating the problem and the solution or decision-making outcomes. In some cases, stakeholders also co-generate – with expert modelers – the shared representation or model.

Here, I discuss two representations generated, respectively, at the first and last meetings and shown in the figures below. These representations are the result of combining models generated by the participatory modeling experts present at each meeting. Individuals were given the following prompt: “create a model using pen and paper that reflects the participatory modeling process”. The sheets of paper were then collected and aggregated, following which I created a digitized version.

Representation generated at the first meeting by the participatory modeling group (source: Rebecca Jordan)

Representation generated at the last (fourth) meeting by the participatory modelling group (source: Rebecca Jordan)

Comparing the figures generated at the first and last meetings, it can be seen that both feature models, cycles, multiple scales, inclusion, and exclusion of participants.

But there are four major differences. Compared to the last meeting figure, the first meeting figure:

  1. is process oriented, organized as steps,
  2. features explicit theories,
  3. lacks realistic pictures including people, and
  4. lacks explicit mention of researchers.

My impression is that these differences also framed the changes in group discussion during the meeting processes.

One change was that the participatory modeling experts became more comfortable with each other allowing for a more creative flow of ideas and a more comfortable discourse. They also became more familiar with the ideas being represented in the different disciplines and could talk more freely about these ideas.

If we take the two representations as indicative of the change in the way that participants viewed the participatory modeling process, then I suggest that the group became somewhat humbled by the limitations in the research about (and the institutions that house) participatory processes in general. Not only did we read about, and discuss at length, the processes and tools within multiple cases, but we also confronted the socio-economic and political challenges that people across the globe face. In addition, we recognized the complex layers of uncertainty embedded within natural and social systems. The figure from the final meeting depicts a more reflective and personalized perspective on the participatory process that encompasses a much greater scale.

What stood out to me was the increased appreciation for the multiple layers of the processes by which people gather information and learn. While the group began with discussions about the inherent complexity in governance processes and the extent of varying stakeholder needs, the group ended the series of meetings with greater recognition of neurology, cognition, identity, culture, and the researcher biases that are all part of participatory engagement.

While these are personal reflections, I am interested in what you see in the change across the two figures. For me, better capturing the complexity that arose in our discussions has great potential to improve participatory modeling and the research that uses it. What do you think?

Biography: Rebecca Jordan is Professor and Department Chair of Community Sustainability in the College of Agriculture and Natural Resources at Michigan State University, Michigan, USA. She devotes most of her research effort to investigating public learning of science through citizen science and participatory modeling. She was a co-Principal Investigator of the Participatory Modelling pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).

This blog post resulted from the Participatory Modeling pursuit which was part of the theme Building Resources for Complex, Action-Oriented Team Science funded by the National Socio-Environmental Synthesis Center (SESYNC).

Negotiations and ‘normative’ or ‘ethical’ power

Community member post by Lena Partzsch

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Lena Partzsch (biography)

What can we learn from international relations about how ‘normative’ or ‘ethical’ power can be used in successful negotiations, for example, for pathways to sustainability? Here I build on Ian Manners’ (2002) concept of “Normative Power Europe”. He argues that the European Union’s specific history “pre‐disposes it to act in a normative way” (Manners 2002: 242) based on norms such as democracy, rule of law, social justice and respect for human rights. I explore the broader ramifications of the normative power concept for empirical studies and for practical negotiation and collaboration more generally.

First, the concept of normative power implies that the spread of particular norms is perceived as a principal policy goal, whether that relates to foreign policy, environmental policy or other kinds of policy. Continue reading

What can interdisciplinary collaborations learn from the science of team science?

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Suzi Spitzer (biography)

How can we improve interdisciplinary collaborations? There are many lessons to be learned from the Science of Team Science. The following ten lessons summarize many of the ideas that were shared at the International Science of Team Science Conference in Galveston, Texas, in May 2018.

1. Team up with the right people
On the most basic level, scientists working on teams should be willing to integrate their thoughts with their teammates’ ideas. Participants should also possess a variety of social skills, such as negotiation and social perceptiveness. The most successful teams also encompass a moderate degree of deep-level diversity (values, perspectives, cognitive styles) and include women in leadership roles. Continue reading

Structure matters: Real-world laboratories as a new type of large-scale research infrastructure

Community member post by Franziska Stelzer, Uwe Schneidewind, Karoline Augenstein and Matthias Wanner

What are real-world laboratories? How can we best grasp their transformative potential and their relationship to transdisciplinary projects and processes? Real-world laboratories are about more than knowledge integration and temporary interventions. They establish spaces for transformation and reflexive learning and are therefore best thought of as large-scale research infrastructure. How can we best get a handle on the structural dimensions of real-word laboratories?

What are real-world laboratories?

Real-world laboratories are a targeted set-up of a research “infrastructure“ or a “space“ in which scientific actors and actors from civil society cooperate in the joint production of knowledge in order to support a more sustainable development of society.

Although such a laboratory establishes a structure, most discussions about real-world laboratories focus on processes of co-design, co-production and co-evaluation of knowledge, as shown in the figure below. Surprisingly, the structural dimension has received little attention in the growing field of literature.

Overcoming structure as the blind spot

We want to raise awareness of the importance of the structural dimension of real-world laboratories, including physical infrastructure as well as interpretative schemes or social norms, as also shown in the figure below. A real-world laboratory can be understood as a structure for nurturing niche development, or a space for experimentation that interacts (and aims at changing) structural conditions at the regime level.

Apart from this theoretical perspective, we want to add a concrete “infrastructural” perspective, as well as a reflexive note on the role of science and researchers. Giddens’ use of the term ‘structure’ helps to emphasize that scientific activity is always based on rules (eg., rules of proper research and use of methods in different disciplines) and resources (eg., funding, laboratories, libraries).

The two key challenges of real-world laboratories are that:

  1. both scientists and civil society actors are involved in the process of knowledge production; and,
  2. knowledge production takes place in real-world environments instead of scientific laboratories.
Franziska Stelzer (biography)

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Uwe Schneidewind (biography)

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Karoline Augenstein (biography)

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Matthias Wanner (biography)

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Continue reading

When are scientists neutral experts or strategic policy makers?

Community member post by Karin Ingold

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Karin Ingold (biography)

What roles can science and scientific experts adopt in policymaking? One way of examining this is through the Advocacy Coalition Framework (Sabatier and Jenkins-Smith 1993). This framework highlights that policymaking and the negotiations regarding a political issue—such as reform of the health system, or the introduction of an energy tax on fossil fuels—is dominated by advocacy coalitions in opposition. Advocacy coalitions are groups of actors sharing the same opinion about how a policy should be designed and implemented. Each coalition has its own beliefs and ideologies and each wants to see its preferences translated into policies. Continue reading