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Computers, productive agency, and the effort after shared meaning

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COMPUTERS PROVIDE FRESH OPPORTUNITIES for enhancing and understanding collaborative learning. They permit new research methodologies such as simulations of cooperating agents, and they present new design challenges including computerized peers and supporting collaborations across cultural boundaries. This article has two goals. One goal is to offer recent examples and findings that may help design computer-supported collaboration. The other is to begin a theoretical discussion that focuses on the individuals who collaborate. This differs from much of the collaborative and cooperative learning literature that emphasizes the rules and structures for enforcing collaboration (e.g., group roles and joint accountability). We start with individuals, because we believe that much of the learning that occurs during collaboration develops out of individuals’ efforts to share meaning and understand one another. We consider prerequisites to people’s effort to share meaning, and we particularly focus on the important role of the productive agency that leads people to contribute rather than just borrow knowledge. We consider the type of knowledge likely to develop during collaboration, and we suggest ways to prepare and help people learn from the language that permeates collaborations as well as formal classroom lectures and texts.

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  1. Baker, M., Hansen, T., Joiner, R., & Traum, D. (1999). The role of grounding in collaborative learning tasks. In P. Dillenbourg (Ed.),Collaborative learning: Cognitive and computational approaches (pp. 31–63). Oxford, UK: Elsevier Science.

  2. Barron, B.J. (2000). Problem solving in video-based microworlds: Collaborative and individual outcomes of high achieving sixth grade students.Journal of Educational Psychology, 92, 391–398.

  3. Bartlett, F.C. (1932).Remembering: A study in experimental psychology. Cambridge: Cambridge University Press.

  4. Bereiter, C., & Scardamalia, M. (1989). Intentional learning as a goal of instruction. In L.B. Resnick (Eds.),Knowing learning, and instruction: Essays in honor of Robert Glaser (pp. 361–392). Hillsdale, NJ: Lawrence Erlbaum Associates.

  5. Biswas, G., Schwartz, D.L., Bransford, J.D., & The Teachable Agents Group at Vanderbilt. (in press). From SAD Environments to AI. To appear in K. Forbus & P. Feltovich (Eds.),Smart machines in education: The coming revolution in educational technology. Menlo Park, CA: AAAI/MIT Press.

  6. Bransford, J.D., & Johnson, M.K. (1972). Contextual prerequisites for understanding: Some investigations of comprehension and recall.Journal of Verbal Learning and Verbal Behavior, 11, 717–726.

  7. Bransford, J.D., & Nitsch, K.E. (1978). Coming to understand things we could not previously understand. In J.F. Kavanagh & W. Strange (Eds.),Speech and language in the laboratory, school and clinic. Cambridge, MA: MIT Press.

  8. Cassell, J., Ananny, M., Basu, A., Bickmore, T., Chong, P., Mellis, D., Ryokai, K., Vilhjalmsson, H., Smith, J., & Yan, H. (April, 2000). Shared reality: Physical collaboration with a virtual peer.In Proceedings of the Conference on Human Factors in Computing Systems (CHI). Amsterdam, NL.

  9. Cognition & Technology Group at Vanderbilt [CTGV] (1997).The Jasper project. Mahwah, NJ: Lawrence Erlbaum Associates.

  10. Gibson, E. (1969).Principles of perceptual learning and development. NY: Meredith.

  11. Joiner, R., Issroff, K., & Demiris, J. (1999). Comparing human-human and robot-robot interactions. In P. Dillenbourg (Ed.),Collaborative learning: Cognitive and computational approaches (pp. 81–102). Oxford, UK: Pergamon.

  12. Kelly, H.H., & Thibaut, J. W. (1969). Group problem solving. In G. Lindzey & E. Aronson (Eds.),The handbook of social psychology, Second edition (pp. 1–101). Reading, MA: Addison-Wesley.

  13. Lin, X.D. (in press). Reflective adaptation of a technology artifact: A case study of classroom change.Cognition & Instruction.

  14. Lin, X.D., & Hatano, G. (in press). Cross-cultural adaptation of educational technology. In T. Koschmann, R. Hall, & N. Miyake (Eds.),CSCL2: Carrying Forward the Conversation. Hillsdale, NJ: Lawrence Erlbaum Associates.

  15. Lin, X.D., & Schwartz, D.L. (2000).Presenting culture. Manuscript in preparation.

  16. Marx, K. (1939/1973).Grundrisse. (M. Nicolaus, Trans). NY: Random House.

  17. McClellan, C. (1971). Feuding and warfare among Northwestern Athapaskans. In A. McFadyen Clark (Ed.),Proceedings: Northern Athapaskan Conference. National Museum of Man Mercury Series Canadian Ethnology Service, Paper No. 27, Vol. 1.

  18. Moore, J.L., & Schwartz, D.L. (1998). On learning the relationship between quantitative properties and symbolic representations. In A. Bruckman, M. Guzdial, J. Kolodner, & A. Ram (Eds.),Proceedings of the International Conference of the Learning Sciences (pp. 209–214). Charlottesville, VA: AACE.

  19. Moore, J.L., & Schwartz, D.L. (2000).Understanding the relationship between representations and their quantitative referents: A study in the domain of statistics. Manuscript submitted for publication.

  20. Oshima, J., & Oshima, R. (in press). Coordination of asynchronous and synchronous communication: Differences in qualities of knowledge advancement discourse between experts and novices. In T. Koschmann, R. Hall, & N. Miyake (Eds.),CSCL2: Carrying Forward the Conversation. Hillsdale, NJ: Erlbaum.

  21. Piaget, J. (1972). Intellectual evolution from adolescence to adulthood.Human Development, 15, 1–12.

  22. Ploetzner, R., Dillenbourg, P., Preier, M., & Traum, D. (1999). Learning by explaining to oneself and to others. In P. Dillenbourg (Ed.),Collaborative learning: Cognitive and computational approaches (pp. 103–121). Oxford, UK: Pergamon.

  23. Robertson, S.P., Zachary, W., & Black, J.B. (Eds.). (1990).Cognition, computing, and cooperation. Norwood, NJ: Ablex.

  24. 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.

  25. Schwartz, D.L. (1995). The emergence of abstract representations in dyad problem solving.Journal of the Learning Sciences, 4, 321–354.

  26. Schwartz, D.L. (1999). The productive agency that drives collaborative learning. In P. Dillenbourg (Ed.),Collaborative learning: Cognitive and computational approaches (pp. 197–218). Oxford, UK: Pergamon.

  27. Schwartz, D.L., & Bransford, J.D. (1998). A time for telling.Cognition & Instruction, 16, 475–522.

  28. Slavin, R.E. (1983).Cooperative learning. NY: Longman.

  29. Suchman, L. (1987).Plans and situated actions: The problem of human machine communication. NY: Cambridge University Press.

  30. Weiss, G., & Dillenbourg, P. (1999). What is “Multi” in multi-agent learning? In P. Dillenbourg (Ed.),Collaborative learning: Cognitive and computational approaches (pp. 64–80). Oxford, UK: Pergamon.

  31. Vye, N., Schwartz, D., Bransford, J., Barron, B., Zech, L., & CTGV (1998). SMART environments that support monitoring, reflection, and revision. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.),Metacognition in Educational Theory and Practice (pp. 305–346). Mahwah, NJ: Lawrence Erlbaum Associates.

  32. Vygotsky, L.S. (1978).Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.

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Daniel L. Schwartz is an Associate Professor in the School of Education, Stanford University. He serves in the Psychological Studies in Education program and the Learning, Design, and Technology program. His research explores how people move from untutored mental models to more formal understanding in the domains of mathematics and science. In addition to laboratory and computer-modeling methodologies, Dr. Schwartz creates technology-based interventions to study and foster classroom learning. A theme throughout Dr. Schwartz’s research is how people’s facility for spatial thinking can inform and influence the processes of learning, instruction, assessment and problem solving. He finds that multimedia technologies make it possible to exploit spatial representations and activities in fundamentally new ways, offering an exciting complement to the verbal approaches that dominate educational research and practice. He is currently designing and testing instructional methods and software that can be used in traditional and innovative classrooms.

Xiaodong Lin is an Assistant Professor of Education and Technology at Department of Teaching and Learning, Vanderbilt University. Dr. Lin studies metacognition and problem solving, and the ways that cultural interactions with the help of technology can facilitate understanding and personal reflection. She develops technology-rich learning environments and explores how such environments influence cross-cultural collaboration and reflection. She finds that technologies make it possible for teachers and students from different cultures to collaborate in fundamentally new ways. This offers exciting opportunities for metacognitive awareness. Her most recent research explores the creation of Virtual Learning Environments that permit teachers from different cultures to collaborate. She hopes that these studies will lead to design principles that can transform the obstacles of geographical and cultural distance into new opportunities for learning.

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Schwartz, D.L., Lin, X. Computers, productive agency, and the effort after shared meaning. J. Comput. High. Educ. 12, 3–33 (2001). https://doi.org/10.1007/BF02940954

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  • collaborative learning
  • small group research
  • agency
  • cultural exchange
  • distributed artificial intelligence