Simulating children learning and explaining elementary heat transfer phenomena: A multistrategy system at work

  • Filippo Neri
Regular Papers Applications of ML
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)


The multistrategy learning system WHY is used as a testbed for investigating a computational cognitive model of conceptual change in children learning elementary physics'. Goal of the simulation is to support the cognitive scientist's investigation of learning in humans.

The student's mental model is manually inferred by the cognitive scientist, and by interacting with WHY, from a sequence of interviews collected along a period of eleven teaching sessions. The hypothesized cognitive models are based on a theory of conceptual change, derived from psychology results and educational experiences, which accounts for the evolution of the student's knowledge over a learning period.

The multistrategy learning system WHY, able to handle domain knowledge (including a causal model of the domain), has been chosen as tool for the interactive simulation of the cognitive models evolution. The system is able to model both the answers and the causal explanations given by the children. An example of modelisation of an observed conceptual change is provided.


cognitive modelling multistrategy learning conceptual change causality 


  1. Baffes P. T. and Mooney R. J. (1996). “A Novel Application of Theory Refinement to Student Modelling”. Proc. of Thirteenth National Conference on Artificial Intelligence (Portland, OR), pp. 403–408.Google Scholar
  2. Caravita S. and Halldén O. (1994). “Re-framing the Problem of Conceptual Change”. Learning and Instruction, 4, 89–111.CrossRefGoogle Scholar
  3. Chi M. T. H., Slotta J. D. and de Leeuw N. (1994). “From Things to Processes: A Theory of Conceptual Change for Learning Science Concepts”. Learning and Instruction, 4, 27–43.CrossRefGoogle Scholar
  4. diSessa A. (1993). “Toward an Epistemology of Physics”. Cognition and Instruction, 10, 105–225.Google Scholar
  5. Forbus K.D. and Gentner D. (1986). “Learning Physical Domains: Toward a Theoretical Framework”. In R. Michalski, J. Carbonell & T. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan Kaufmann, Los Altos, CA, pp. 311–348.Google Scholar
  6. Giordana A. and Neri F., Saitta L. and Botta M. (1997). “Integrating Multiple Learning Strategies in First Order Logics”. Machine Learning, 27, 209–240.CrossRefGoogle Scholar
  7. Mitchell T., Keller R., Kedar-Cabelli S. (1986). “Explanation Based Generalization”, Machine Learning, 1, 47–80.Google Scholar
  8. Murphy L.G. and Medin D.L. (1985). “The Role of Theories in Conceptual Coherence”. Psychological Review, 92, 289–316.PubMedGoogle Scholar
  9. Neri F., Saitta L. and Tiberghien A. (1997a). “Modelling Physical Knowledge Acquisition in Children with Machine Learning”. Proc. of 19th Annual Conference of the Cognitive Science Society, Stanford (CA), Morgan Kaufmann, pp. 566–571.Google Scholar
  10. Newell A. (1990). Unified Theories of Cognition, Harvard University Press, Cambridge, MA.Google Scholar
  11. Rumelhart D. E. and Norman D. A. (1977). “Accretion, Tuning and Restructuring: Three modes of Learning”, in Cotton J. W. and Klatzky R. L. (Eds.), Semantic Factors in Cognition, Erlbaum (Hillsdale, NJ).Google Scholar
  12. Sage S. and Langley P. (1983). “Modeling Cognitive Development on the Balance Scale Task”. Proc. 8th Int. Joint Conf. on Artificial Intelligence (Karlsruhe, Germany), pp. 94–96.Google Scholar
  13. Saitta L., Botta M., Neri F. (1993). “Multistrategy Learning and Theory Revision”. Machine Learning, 11, 153–172.Google Scholar
  14. Saitta L., Neri F. and al. (1995). “Knowledge Representation Changes in Humans and Machines”. In P. Reimann and H. Spada (Eds.), Learning in Humans and Machines: Towards an Interdisciplinary Learning Science, Elsevier (Oxford), pp. 109–128.Google Scholar
  15. Saitta L., Neri F. and Tiberghien A. (1997). “World Model Construction in Children during Physics Learning”. Proc. of International Symposium on Methodologies for Intelligent Systems '97 (ISMIS 97), Lecture Notes in Artificial Intelligence series, Springer Verlag (Berlin, Germany), in press.Google Scholar
  16. Schmidt W.C. and Ling C.X. (1996). “A Decision-Tree Model of Balance Scale Development”. Machine Learning, 24, 203–230.Google Scholar
  17. Shultz T.R., Mareschal D. and Schmidt W. (1994). “Modeling Cognitive Developmmnt on Balance Scale Phenomena”. Machine Learning, 16, 57–86.Google Scholar
  18. Sleeman D., Hirsh H., Ellery I. and Kim I. (1990). “Extending Domain Theories: two case Studies in Student Modeling”. Machine Learning, 5, 11–37.Google Scholar
  19. Tiberghien A. (1989). “Learning and Teaching at Middle School Level of Concepts and Phenomena in Physics. The Case of Temperature”. In H. Mandl, E. de Corte, N. Bennett and H.F. Friedrich (Eds.), Learning and Instruction. European Research in an International Context, Volume 2.1, Pergamon Press, Oxford, UK, pp. 631–648.Google Scholar
  20. Tiberghien A. (1994). “Modelling as a Basis for Analysing Teaching-Learning Situations”. Learning and Situations. 4, 71–87.CrossRefGoogle Scholar
  21. Vosniadou S. (1994). “Capturing and Modeling the Process of Conceptual Change”. Learning and Instruction, 4, 45–69.CrossRefGoogle Scholar
  22. Vosniadou S. and Brewer W.F. (1994). “Mental Models of the Day/Night Cycle”. Cognitive Science, 18, 123–183.CrossRefGoogle Scholar
  23. White R. T. (1994). “Commentary Conceptual and Conceptional Change”. Learning and Instruction, 4, 117–121.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Filippo Neri
    • 1
  1. 1.Università di TorinoDipartimento di InformaticaTorinoItaly

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