Three “must have” steps to improve education for collaborative problem solving

Community member post by Stephen M. Fiore

stephen-fiore_aug-2017
Stephen M. Fiore (biography)

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. Across these is the need to attend to the core features of teamwork as identified in the broad research literature on groups and teams.

Systematic use of instructional strategies

First and foremost, researchers should collaborate with educators to make more systematic use of instructional strategies devised to teach components of collaboration. Research on groups and teams has produced methods that focus on team processes relevant to complex forms of work.

For example, knowledge building training emphasizes communicative processes that make explicit the structure of team member knowledge (eg., mental models), as well as assumptions and interpretations team members have about their knowledge. External representations make such knowledge explicit and concrete and build shared understanding.

Also relevant is training that draws attention to the team process following interactions. Team reflexivity training requires that members reflect on prior performance episodes by focusing on met or unmet objectives, strategies used to address task needs, and efficiency of collaborative interactions. Such training could be improved by further research both on effective interventions and on how students come to learn team processes that improve future interactions.

Opportunities for practice

Second, in addition to systematic implementation of instruction on team process, students need opportunities for practice. Collaboration in the classroom is common in science and engineering, and education level and nature of the content will dictate the team and task context.

For example, in introductory science classes, students may collaborate while learning about fundamental physics concepts, how they should be integrated, and applied, for particular problems. At these levels, knowledge is usually unevenly distributed across students such that they need to communicate what each knows as well as their interpretation of what needs to be applied. Teams need to discuss member contributions and evaluate their appropriateness while also using logical analyses to identify and evaluate solutions. In these stages of learning, basic interpersonal competencies associated with relationship management (eg., encourage participation) and communication (eg., listening to learn), are needed.

At more advanced levels, students address problems that require richer domain knowledge, as well as connection to more complicated scenarios. For example, upper level students might collaborate on complex socio-environmental problems such as overfishing wherein stakeholder factors necessitate consideration of species population dynamics and local economies. At these levels, collaboration requires sophisticated forms of perspective taking to consider alternative views of problem elements.

Considering the need to provide more structured practice opportunities, problem-based learning is a method tested in a variety of settings with meta-analytic support documenting effectiveness. Teams work on real-world problems, first discussing any lack of understanding and identifying gaps in knowledge. From this, they form explicit learning goals and collaborate to gather and integrate knowledge necessary to produce a solution. Research must thoroughly examine these in the classroom to understand how they can best provide the contextual grounding that fosters integration of collaboration skills.

Assessment to measure team work

Finally, more consistent assessments that measure teamwork to provide diagnostic feedback on collaboration are also necessary. To achieve this, there should be a more systematic integration of methods on team training with the educational programs devised for learning to work in teams. This includes consideration of self-ratings of soft skills as well as peer-ratings that assess categories of team involvement like contribution to the team’s work and keeping the team on track. Also needed are assessments of interpersonal competencies such as conflict resolution (eg., reactions to conflict) and assertive communication (eg., addressing differences without intimidation).

Critical to this assessment is ensuring students receive feedback regularly, can compare it to self-assessments, and have opportunities to calibrate it in future collaborations. Research must explore how to incentivize collaborative problem solving skills and integrate grades on collaboration into overall student assessment.

Concluding questions

Do you have successful experiences of teaching and assessing team work to share? What are the key concepts that you teach? Which pedagogical strategies have you found helpful? What questions must the burgeoning “Science of Team Science” pursue to ensure effectiveness in collaborative problem solving?

To find out more:
Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W. and Hesse, F. W. (2018). Advancing the science of collaborative problem solving. Psychological Science in the Public Interest, 19, 2: 59-92. Online:  https://doi.org/10.1177/1529100618808244

Biography: Stephen M. Fiore PhD is Director, Cognitive Sciences Laboratory, and Professor with the University of Central Florida’s Cognitive Sciences Program in the Department of Philosophy and Institute for Simulation and Training. He is Past-President of the Interdisciplinary Network for Group Research, a founding committee member for the annual Science of Team Science Conference and founding board member for the International Network for the Science of Team Science. He maintains a multidisciplinary research interest that incorporates aspects of the cognitive, social, organizational, and computational sciences in the investigation of learning and performance in individuals and teams.

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. Continue reading

Five principles of holistic science communication

Community member post by Suzi Spitzer

suzi-spitzer.jpg
Suzi Spitzer (biography)

How can we effectively engage in the practice and art of science communication to increase both public understanding and public impact of our science? Here I present five principles based on what I learned at the Science of Science Communication III Sackler Colloquium at the National Academy of Sciences in Washington, DC in November 2017.

1. Assemble a diverse and interdisciplinary team

  1. Scientists should recognize that while they may be an expert on a particular facet of a complex problem, they may not be qualified to serve as an expert on all aspects of the problem. Therefore, scientists and communicators should collaborate to form interdisciplinary scientific teams to best address complex issues.
  2. Science is like any other good or service—it must be strategically communicated if we want members of the public to accept, use, or support it in their daily lives. Thus, research scientists need to partner with content creators and practitioners in order to effectively share and “sell” scientific results.
  3. Collaboration often improves decision making and problem solving processes. People have diverse cognitive models that affect the way each of us sees the world and how we understand or resolve problems. Adequate “thought world diversity” can help teams create and communicate science that is more creative, representative of a wider population, and more broadly applicable.

Continue reading

Sharing mental models is critical for interdisciplinary collaboration

Community member post by Jen Badham and Gabriele Bammer

badham
Jen Badham (biography)

What is a mental model? How do mental models influence interdisciplinary collaboration? What processes can help tease out differences in mental models?

Mental models

Let’s start with mental models. What does the word ‘chair’ mean to you? Do you have an image of a chair, perhaps a wooden chair with four legs and a back, an office chair with wheels, or possibly a comfortable lounge chair from which you watch television? Continue reading

ICTAM: Bringing mental models to numerical models

Community member post by Sondoss Elsawah

sondoss-elsawah
Sondoss Elsawah (biography)

How can we capture the highly qualitative, subjective and rich nature of people’s thinking – their mental models – and translate it into formal quantitative data to be used in numerical models?

This cannot be addressed by a single method or software tool. We need multi-method approaches that have the capacity to take us through the learning journey of eliciting and representing people’s mental models, analysing them, and generating algorithms that can be incorporated into numerical models.

More importantly, this methodology should allow us to see in a transparent way the progression on this learning journey. Continue reading

Models as narratives

Community member post by Alison Singer

singer
Alison Singer (biography)

I don’t see the world in pictures. I mean, I see the world in all its beautiful shapes and colors and shadings, but I don’t interpret the world that way. I interpret the world through the stories I create. My interpretations of these stories are my own mental models of how I view the world. What I can do then, to share this mental model, is create a more formalized model, whether it is a simple picture (in my case a very badly drawn one), or a system dynamics model, or an agent-based model. People think of models as images, as representations, as visualizations, as simulations. As tools to represent, to simplify, to teach, and to share. And they are all these things, and we need them to function as these things, but they are also stories, and can be interpreted and shared as such. Continue reading