We know that reflecting can make a marked difference to the quality of our collective endeavour. However, in the daily busyness of inter- and trans- disciplinary research collaborations, time for reflection slides away from us as more immediate tasks jostle for attention. What would help us put into regular practice what we know in theory about prioritising time to reflect and learn?
Discomfort sometimes provides the necessary nudge in the ribs that reminds us to keep reflecting and learning. The discomfort of listening to the presentation of a colleague you like and respect, but having very little idea what they’re talking about.
Many environmental, social, and public health problems require collaborative problem solving because they are too complex for an individual to work through alone. This requires a research and technical workforce that is better prepared for collaborative problem solving. How can this be supported by educational programs from kindergarten through college? How can we ensure that the next generation of researchers and engineers are able to effectively engage in team science?
Drawing from disciplines that study cognition, collaboration, and learning, colleagues and I (Graesser et al., 2018) make three key recommendations to improve research and education with a focus on instruction, opportunities to practice, and assessment.
Tensions inevitably arise in inter- and transdisciplinary research. Dealing with these tensions and resulting conflicts is one of the hardest things to do. We are meant to avoid or get rid of conflict and tension, right? Wrong!
Tension and conflict are not only inevitable; they can be a source of positivity, emergence, creativity and deep learning. By tension we mean the pull between the seemingly contradictory parts of a paradox, such as parts and wholes, stability and chaos, and rationality and creativity.
Can a dive into the philosophical depths of transdisciplinarity provide an orientation to the fundamental purpose and need for transdisciplinarity?
The earlier philosophers of transdisciplinarity – such as Erich Jantsch (1980), Basarab Nicolescu (2002), and Edgar Morin (2008) – all aim to stretch or transcend the dominant Western paradigm, which arises in part from Aristotle’s rules of good thought. Aristotle’s rules of good thought, or his epistemology, state essentially that to make meaning in the world, we must see in terms of difference; we must make sense in terms of black and white, or dualistic and reductive thinking.
What are the key lessons for building a successful collaborative team? A new version of the Collaboration and Team Science Field Guide (Bennett et al., 2018) provides ten top take aways:
1. TRUST
It is almost impossible to imagine a successful collaboration without trust. Trust provides the foundation for a team. Trust is necessary for establishing other aspects of a successful collaboration such as psychological safety, candid conversation, a positive team dynamic, and successful conflict management.
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.
What can we learn when we bring together different insights from the rich and diverse traditions of action-oriented research? Will this help us more effectively understand and navigate our way through a world of change to ensure knowledge production contributes more directly to societal needs?
In a recent publication (Fazey et al., 2018), we explored the critical question of how to develop innovative, transformative solutions and knowledge about how to implement them. Addressing these questions requires much more engagement with more practical forms of knowledge, as well as learning from action and change in much more direct ways than currently occurs in academia. It is like learning to ride a bicycle, which can’t be done just by watching a powerpoint presentation, and which requires learning by “getting hands dirty” and by falling off and starting again.
How can we adequately prepare and train students to navigate transdisciplinary environments? How can we develop hybrid spaces in our universities that are suitable for transdisciplinary education?
These questions were considered by a plenary panel, which I organised and chaired at the International Transdisciplinarity Conference 2017 at Leuphana University, Germany. Three major educational requirements were identified:
long-term collaborations with businesses, as well as non-governmental, governmental and community organisations
Many voices are expressed when researchers from different backgrounds come together to work on a new project and it may sound like cacophony. All those voices are competing to be heard. In addition, researchers make different assumptions about people and data and if these assumptions are not brought to light, the project can reach an impasse later on and much time can be wasted.
So how can such multivocality be positive and productive, while avoiding trouble? How can multiple voices be harnessed to not only achieve the project’s goals, but also to make scientific progress?
How can we identify and evaluate decision forks in a modelling project; those points where a different decision might lead to a better model?
Although modellers often follow so called best practices, it is not uncommon that a project goes astray. Sometimes we become so embedded in the work that we do not take time to stop and think through options when decision points are reached.
One way of clarifying thinking about this phenomenon is to think of the path followed. The path is the sequence of steps actually taken in developing a model or in a problem solving case. A modelling process can typically be carried out in different ways, which generate different paths that can lead to different outcomes. That is, there can be path dependence in modelling.
Recently, we have come to understand the importance of human behaviour in modelling and the fact that modellers are subject to biases. Behavioural phenomena naturally affect the problem solving path. For example, the problem solving team can become anchored to one approach and only look for refinements in the model that was initially chosen. Due to confirmation bias, modelers may selectively gather and use evidence in a way that supports their initial beliefs and assumptions. The availability heuristic is at play when modellers focus on phenomena that are easily imaginable or recalled. Moreover particularly in high interest cases strategic behaviour of the project team members can impact the path of the process.
I am a firm believer in looking at interdisciplinary collaboration and knowledge exchange – or impact generation – as processes. If you can see something as a process, you can learn about it. If you can learn about it, you can do it better!
I find that this approach helps people to feel enfranchised, to believe that it is possible for them to open up what might have seemed to be a static black box and achieve understanding of the dynamics of how nouns like ‘interdisciplinarity’ or ‘knowledge exchange’ or ‘research impact’ can actually come to be.
Storytelling ethnography is a valuable tool if your research traverses several disciplines and aims for insights that transcend all of them. Stories not only integrate knowledge from diverse disciplines, but can also “change the way people act, the way they use available knowledge” (Griffiths 2007).
The special qualities of transdisciplinarity are:
its potential for integrative inquiry and emergent solutions,
its engagement with community and other non-academic knowledges, and
the breadth of its outcomes for researchers, participants and the wider community.
These are also qualities of what I call storytelling ethnography.
