Collaborative Learning

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Contributors: Jeremy Roschelle, Dan Suthers, Shuchi Grover
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Learning to explain, justify, critique, etc. are essential skills for today’s citizens, for scientists, and in many other careers. These activities are intrinsically social. Further, conceptually challenging content is often best learned by working together with other learners. However, merely asking students to “work together” is not enough to lead to positive learning outcomes. Tools and activities must be designed to enable, structure, and guide social interactions to facilitate effective learning.

Collaborative learning engages students to work as a team in learning together, and is not just a matter of dividing up work among members of a team. When collaborative learning is working well, students engage in building on each others’ contribution, and individuals learn from their team as the team advances a shared outcome. Effective collaborative learning teams are able to manage both their team relationships and progress on tasks, and are able to monitor and reflect on their process. Terms used to indicate the essence of learning together include: joint problem solving, intersubjectivity, shared/collective/group/distributed cognition, collective consciousness, and transactive discourse.

Theories of collaborative learning give shape to design and analysis of collaborative learning. From a constructivist perspective, learning occurs as students make sense of their experience. A social experience can be rich in new ideas, conflict with one’s own ideas, and high expectations for the quality of ideas. From a social cognition perspective, learners’ efforts to find common ground and share information with others can creates optimal conditions for developing knowledge, with appropriate levels of challenge and support. A participatory perspective focuses on the process of becoming an effective member of a community, and includes learning the social norms, practices, language, activities and tools of the community — while also developing one’s individual skill in doing the work of the community. Each of these theories has had a profound influence on how designers and researchers address collaborative learning.

The core agenda of collaborative learning in cyberlearning is the design and investigation of social technologies to influence the interactions of students in groups, and thereby to increase learning in the group. Targets for design and investigation can include motivational, social, and cognitive dimensions of interacting in groups. Tools often aim to better support specific features of social interaction (argumentation, negotiation, communication, explanation), enable groups to represent social knowledge (improving social awareness and helping students in capturing, referring to, visualizing, organizing, analyzing, critiquing, etc. each other’s ideas.), or guide teams through activities (scripting, scaffolding or coaching). Tools often make features of collaborative learning more visible to members of the group and more available for action by the group. Designs for collaborative learning recognize that students sometimes work as individuals, in small groups and in large ensembles (such as a classroom or a online discussion group) and that effective environments support students across these modalities. Some designs seek to help teachers orchestrate many simultaneous or sequenced social learning activities.

Important elements of collaborative learning include:

  • motivation for the effort of working with another learner
  • joint attention, students are looking at the same things
  • mutual engagement, students are actively involved with each other
  • individual agency, each student has responsibility and opportunity for action in the team and for learning from the team’s work
  • group action and accountability, students are discussing, making or problem solving together and the result of their group’s work matters
  • design of roles, responsibilities and measures of progress
  • constructive discourse patterns, including making and acknowledging contributions, finding common ground, providing and receiving help, etc.
  • monitoring and reflecting on teamwork (i.e., meta-cognition and self-regulation)
  • orchestrating participation across activities, places, roles, etc.

Research that investigates collaborative learning almost always include methods for analyzing students’ interactions, conversations, and participation in teams or groups — analyzing the process of collaborative learning is important. Research can look at learning as students interact face-to-face or at a distance. Data is often captured by audio or video recording, but also by capturing what students do with technology. Emerging technologies such as eye-tracking can also help capture joint attention. Outcome measures can include individual growth or can focus on the increased capacity of the group — and outcomes can be cognitive (knowledge and skill), interpersonal (membership in a group), or social (skills in learning together). Research can be framed as (a) iterative, design-based research or (b) as comparative experiments or (c) in terms of socio-cultural analysis.

Researchers in collaborative learning often share their work through the activities of the International Society of the Learning Sciences, including a conference series and journal.


Scripting and Self-Regulation. Learners do not often organize themselves well for collaborative learning spontaneously. A strong literature in computer-supported collaborative has designed and investigated the impacts of scripts that structure the interaction of group members so as to improve collaborative learning processes and outcomes. A emerging tension concerns how groups can learn to regulate their own learning over time, so that less explicit scripting is necessary.

Emerging Technologies. Traditional collaborative learning research often involved computers and conventional networks. Emerging technologies can focus on devices that support mutual awareness, tangible computing, mobiles devices, design of furniture and rooms (as well as virtual spaces) for collaborative learning, and ways to leverage social networking.

Tensions around “Networked Individualism.” Our increasingly networked society tends to overuse the word “collaboration” wherever communication occurs. In contrast, work in collaborative learning has emphasized strong relationships among learners, joint action, and collective outcomes. How can collaborative learning leverage the strength and ubiquity of weak ties? Conversely, how can more emphasis on learning together be supported in commonplace, large scale use of communication technology? How should theories expand to acknowledge the dynamic boundaries between individual and social learning?

Challenges in Methods and Measurement. As previously mentioned, collaborative learning often occurs across different groupings, different times, and different spaces. Multiple units of analysis are often important, and bridging analyses at different levels of interaction is difficult. Accounting for both individual and group outcomes of collaborative learning remains an open issue. Analysis of student interaction in teams has often involved laborious methods, and finding ways to simplify or automate analysis of collaborative learning processes is important.


References and key readings documenting the thinking behind the concept, important milestones in the work, foundational examples to build from, and summaries along the way.

See also: publications from NSF-funded cyberlearning projects.

Key Readings

Barron, B. & Roschelle, J. (2009). Shared cognition. In Anderman, Eric (ed.), Psychology of Classroom Learning: An Encyclopedia, pp. 819-823. Detroit: Macmillan Reference USA.

Dillenbourg, P. (1999). What do you mean by collaborative learning?. Collaborative-learning: Cognitive and Computational Approaches., 1-19.

Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In Technology-enhanced learning (pp. 3-19). Springer Netherlands.

Fischer, F., Kollar, I., Mandl, H., & Jaake, J. M. (2007). Scripting Computer-Supported Collaborative Learning: Cognitive, Computational and Educational Perspectives. New York: Springer.

Kirschner, P. A., & Erkens, G. (2013). Toward a framework for CSCL research. Educational Psychologist, 48(1), 1-8.

Resnick, L. B. Levine, J. M., & Teasley, SD (Eds.).(1991). Perspectives on socially shared cognition.

Roschelle, J. (1992). Learning by collaborating: Convergent conceptual change. The journal of the learning sciences, 2(3), 235-276.

Roschelle, J., & Teasley S. D. (1995). The construction of shared knowledge in collaborative problem solving. In C. E. O’Malley (Ed), Computer-supported collaborative learning. (pp. 69-97). Berlin: Springer-Verlag.

Stahl, G., Koschmann, T., & Suthers, D. D. (2006). Computer-supported collaborative learning: An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409-426). Cambridge, UK: Cambridge University Press.

Suthers, D. D. (2006). Technology affordances for intersubjective meaning-making: A research agenda for CSCL. International Journal of Computer Supported Collaborative Learning, 1(3), 315-337.

Publications from NSF-funded Cyberlearning Projects

Swartz, M., Li, J., & Wanless, S. (2016, March). Peg + Cat: Adventures in learning. Poster session presented at the 2016 Advancing Informal STEM Learning (AISL) PI Meeting, Bethesda, MD.

Waters, A., Studer, C., & Baraniuk, R. (2014). Collaboration-Type Identification in Educational Datasets. Journal of Educational Data Mining, Vol. 6(1), pp. 28-52.

Adamson, D., Dyke, G., Jang, H., & Rosé, C. P. (2014). Towards an agile approach to adapting dynamic collaboration support to student needs. International Journal of Artificial Intelligence in Education, Vol. 24(1), pp. 92-124.

Carroll, J. M., Jiang, H., & Borge, M. (2015). Distributed collaborative homework activities in a problem-based usability engineering course. Education and Information Technologies, Vol. 20(3), pp. 589-617.

Brown, R., Lynch, C. F., Eagle, M., Albert, J., Barnes, T., Baker, R., & McNamara, D. (2015). Good communities and bad communities: Does membership affect performance. In Proceedings of the 8th International Conference on Educational Data Mining (pp. 612-614).Madrid, Spain: Educational Data Mining.

Brown, R., Lynch, C., Wang, Y., Eagle, M., Albert, J., Barnes, T., & McNamara, D. (2015, June). Communities of performance & communities of preference. In Proceedings of the 2nd International Workshop on Graph-Based Educational Data Mining. Madrid, Spain: Educational Data Mining.

D’Angelo, C. M., Roschelle, J., Bratt, H., Shriberg, L., Richey, C., Tsiartas, A., & Alozie, N. (2015). Using students’ speech to characterize group collaboration quality. In Proceedings of The Computer Supported Collaborative Learning (CSCL) Conference 2015. Gothenburg, Sweden: Computer Support Collaborative Learning.

Kang, S., Norooz, L., Oguamanam, V., Plane, A., Clegg, T. L., & Froehlich, J. E. (2016). SharedPhys: Live Physiological Sensing, Whole-Body Interaction, and Large-Screen Visualizations to Support Shared Inquiry Experiences.

Stahl, G., Mantoan, A., & Weimar, S. (2013). Demo: Collaborative dynamic mathematics in virtual math teams. In Proceedings of the International Conference of Computer-Supported Collaborative Learning, Madison, WI: Computer-Supported Collaborative Learning.

Flood, V. J., Neff, M. & Abrahamson, D. (2015). Boundary Interactions: Resolving interdisciplinary collaboration challenges using digitized embodied performances. Proceedings of the Computer Supported Collaborative Learning Conference. Gothenberg, Sweden: Computer Supported Collaborative Learning.


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