People power: A human-computer collaborative learning system

  • Pierre Dillenbourg
  • John A. Self
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 608)


This paper reports our research work in the new field of human-computer collaborative learning (HCCL). The general architecture of an HCCL is defined. An HCCL system, called People Power, has been implemented in CLOS. It contains a micro-world in which the learner can create an electoral system and simulate elections. The learner's task is to infer relations between the features of the electoral system and the distribution of seats. The human learner collaborates with a computational learner. The collaboration between learners is modelled as ‘socially distributed cognition’ (SDC). We view a pair of learners as a single cognitive agent whose components are distributed over two brains. This model maps inter-people and intra-people communication processes and thereby proposes an explanation of how the former generates the latter: the pattern of arguments that emerge from dialogue is reused by the artificial learner when it reasons alone. Reasoning is implemented as a dialogue with oneself. We report some results of the first experiments we have conducted.


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  1. BAKER, M. (1992) The collaborative construction of explanations. Paper presented to “Deuxièmes Journées Explication du PRC-GDR-IA du CNRS”, Sophia-Antipolis, June 17–19 1992.Google Scholar
  2. BLAYE, A. (1988) Confrontation socio-cognitive et resolution de problemes. Doctoral dissertation, Centre de Recherche en Psychologie Cognitive, Université de Provence, 13261 Aix-en-Provence, France.Google Scholar
  3. BLAYE, A., LIGHT, P., JOINER, R. & SHELDON, S. (1991) Collaboration as a facilitator of planning and problem solving on a computer based task. British Journal of Psychology, 9, 471–483.Google Scholar
  4. CHAN, T.-W. & Baskin, A.B. (1988) “Studying with the prince”, The computer as a learning companion. Proceedings of the International Conference on Intelligent Tutoring Systems (pp. 194–200), June 1–3. Montreal, Canada.Google Scholar
  5. CLARK, H.H. & BRENNAN S.E. Grounding in Communication. In L. Resnick, J. Levine and S. Teasley. Perspectives on Socially Shared Cognition (pp. 127–149). Hyattsville, MD: American Psychological Association.Google Scholar
  6. DE HAAN, A. & OPPENHUIZEN, C.J. (1990) RITS, cooperative problem solving with computers, a pragmatic approach. Proceedings of the Second ‘Congres Européen Mutli-Media, IA et Formation', September 24–26. Lille, France.Google Scholar
  7. DILLENBOURG, P. (1991) Human-Computer Collaborative Learning. Doctoral dissertation. Department of Computing. University of Lancaster, Lancaster LA14YR, UK.Google Scholar
  8. DOISE, W. & MUGNY, G. (1984) The social development of the intellect. Oxford: Pergamon Press.Google Scholar
  9. DURFEE, E.H., LESSER, V.R. & CORKILL, D.D. (1989) Cooperative Distributed Problem Solving. In A. Barr, P.R. Cohen & E.A. Feigenbaum (Eds) The Handbook of Artificial Intelligence, (Vol. IV, pp. 83–127). Reading, Massachusetts: Addison-Wesley.Google Scholar
  10. HUTCHINS, E. (1991) The Social Organization of Distributed Cognition. In L. Resnick, J. Levine and S. Teasley. Perspectives on Socially Shared Cognition (pp. 383–307). Hyattsville, MD: American Psychological Association.Google Scholar
  11. MINSKY, M (1987) The society of mind. London: William Heinemann Ltd.Google Scholar
  12. MIYAKE, N. (1986) Constructive Interaction and the Iterative Process of Understanding. Cognitive Science, 10, 151–177.Google Scholar
  13. O'MALLEY, C. (1987) Understanding explanation. Paper presented at the third CeRCLe Workshop, Ullswater, UK.Google Scholar
  14. ROSCHELLE J. (to appear) Learning by Collaborating: Convergent Conceptual Change. To appear in the Journal of the Learning Sciences.Google Scholar
  15. SELF, J.A. (1986) The application of machine learning to student modelling. Instructional Science, 14, 327–338.Google Scholar
  16. SELF, J.A. & HARTLEY J.R. (1989) Guided Discovery Learning Systems in Intelligent Computer-Aided Instruction. Final Report of the GDLS project. Department of Computing, University of Lancaster, Lancaster LA1 4YR, UK.Google Scholar
  17. SUCHMAN, L.A. (1987) Plans and Situated Actions. The problem of human-machine communication. Cambridge: Cambridge University Press.Google Scholar
  18. VYGOTSKY, L.S. (1978), Mind in Society. The Development of Higher Psychological Processes. Edited by M. Cole, V. John-Steiner, S. Scribner & E. Souberman Harvard University Press. Cambridge, Massachusetts: Harvard University Press.Google Scholar
  19. WEIZENBAUM, J. (1966) ELIZA: a computer program for the study of natural language communication between man and machine. Communications of the Association for Computing Machinery, 29 (1), pp. 36–45.Google Scholar
  20. WERTSCH, J.V. (1985) Adult-Child Interaction as a Source of Self-Regulation in Children. In S.R. YUSSEN (Ed). The growth of reflection in Children (pp. 69–97). Madison, Wisconsin: Academic Press.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Pierre Dillenbourg
    • 1
  • John A. Self
    • 2
  1. 1.TECFA, Faculté de Psychologie et des Sciences de l'EducationUniversity of GenevaSwitzerland
  2. 2.Computing DepartmentUniversity of LancasterUK

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