Abstract
The goal of the reported research is the development of a computational approach that could help a cognitive scientist to interactively represent a learner's mental models, and to automatically validate their coherence with respect to the available experimental data. In a reported case-study, the student's mental models are inferred from questionnaires and interviews collected during a sequence of teaching sessions. These putative cognitive models are based on a theory of knowledge representation, derived from psychological results and educational studies, which accounts for the evolution of the student's knowledge over a learning period. The learning system WHY, able to handle (causal) domain knowledge, shows how to model the answers and the causal explanations given by the learner.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Anderson, J. R. (1985). The Architecture of cognition. Cambridge, MA: Harvard University Press.
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum.
Anzai, Y. & Simon, H. A. (1979). The theory of learning by doing. Psychological Review, 86, 124–140.
Baffes, P. T. & Mooney, R. J. (1996). A novel application of theory refinement to student modelling. Proc. of Thirteenth National Conference on Artificial Intelligence (pp. 403–408) Portland, OR.
Baroglio, C., Botta, M., & Saitta, L. (1994). WHY: A system that learns from a causal model and a set of examples. In R. Michalski & G. Tecuci (Eds.), Machine learning: A multistrategy approach, Vol IV (pp. 319–348) Los Altos, CA: Morgan Kaufmann.
Brown, A. L. (1989). Analogical learning and transfer: What develops? In S. Vosnadiou & A. Ortony (Eds.), Similarity and analogical reasoning, Cambridge, MA: Cambridge University Press. pp. 369–412.
Caravita, S. & Halldèn, O. (1994). Reframing the Problem of Conceptual Change. Learning and instruction, 4, 89–111.
Carey, S. (1983). Conceptual change in childhood. Cambridge, MA: MIT Press.
Carey, S. & Spelke, E. (1994). Domain-specific knowledge and conceptual change. In L. A. Hischfeld & S. A. Gelman (Eds.), Mapping the mind (pp. 169–200). Cambridge, Cambridge University Press.
Chi, M. T. H. (1992). Conceptual change within and across ontological categories: Examples from learning and discovery in science. In R. Giere (Ed.), Cognitive models of science, (pp. 129–160) Minnesota Studies in the Philosophy of Science, Minneapolis, MN: University of Minnesota Press.
Chi, M. T. H., Bassok, M., Lewis M. L., Reiman, P., & Glaser, R. (1989). Self-explanation: how students study an use examples in learning to solve problems. Cognitive Science, 13, 145–182.
Chi, M. T. H., Slotta, J. D., & de Leeuw, N. (1994). From things to processes: A theory of conceptual change for learning science concepts. Learning and instruction, 4, 27–43.
diSessa, A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225.
diSessa, A. (1996). What do “just plain folk” know about physics. In D. Olson & N. Terrance (Eds.), Handbook of education and human development, (pp. 709–730) Blackwell Publ..
Driver, R., Guesne, E., & Tiberghien, A. (1985). Children's ideas in science. Milton Keynes, UK. Open University Press.
Duit, R. (1995). “Constraints on knowledge acquisition and conceptual change the case of physics. Proc. of Symposium on Constraints on Knowledge Construction and Conceptual Change: A Look across Content Domains at 6th European Conference for Research on Learning and Instruction, Nijmegen, The Netherlands.
Dykstra, D. (1992). Studying conceptual change: Constructing new understandings. In R. Duit, F. Goldberg, & H. Niedderer (Eds.), Research in physics learning: Theoretical Issues and Empirical Studies (pp. 40–57) Kiel, Germany: Institut für die Pädagogik der Naturwissenschaften.
Erickson, G. L. (1979). Children's conceptions of heat and temperature. Science Education, 63, 221–230.
Forbus, K. D. & 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 (pp. 311–348) Los Altos, CA: Morgan Kaufmann.
Gertner, A. S., Conati, C., & VanLehn, K. (1998). Procedural help in Andes: Generating hints using a bayesian network student model, Proc. of National Conference on Artificial Intelligence (AAAI'98), Madison (WI), pp. 106–111.
Halloun, I. A. & Hestenes, D. (1987). Modeling instruction in mechanics. American Journal of Physics, 55, 455–462.
Hestenes, D. (1987). Toward a modelling theory of physics instruction. American Journal of Physics, 55, 440–454.
Klahr, D. & Siegler, R. S. (1978). The representation of children's knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), Advances in child development and behavior (pp. 61–116) Academic Press, New York, NY.
Kuhn, T. S. (1971). Les notions de causalitè dans le dèveloppement de la physique. In M. Bunge, F. Halbwachs, J. Piaget, & L. Rosenfeld (Eds.), Les thèories de la causalitè (pp. 7–18) P.U.F., Paris, France.
Laird, P. N. (1983). Mental models: Towards a cognitive science of language, inference, and consciousness, Cambridge, MA: Harvard University Press.
Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986): Chunking in SOAR: The anathomy of a general learning mechanism, Machine Learning, 1, 11–46.
Langley, P. (1987). A general theory of discrimination learning. In D. Klahr, P. Langley, & R. Neches (Eds.), Production system models of learning and development, Cambridge, MA: MIT Press.
Lewis, C. (1987). Composition of productions. In D. Klahr, P. Langley, & R. Neches (Eds.), Production system models of learning and development, Cambridge, MA: MIT Press.
Mitchell, T., Keller, R. M., & Kedar-Cabelli, S. (1986). Explanation-based generalization: A unifying view. Machine Learning, 1, 47–80.
Murphy, L. G. & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92, 289–316.
Neri, F. (1998). Simulating children learning and explaining elementary heat transfer phenomena: a multistrategy system at work. Proc. of European Conference on Machine Learning (ECML98), Lecture Notes in Artificial Intelligence series, vol. 1398 (pp. 67–76).
Neri, F. (1999). Computer aided tracing of childrens physics learning: a teacher oriented view. Proc. of International Joint Conference on Artificial Intelligence 99 (IJCAI99), Stokholm, Sweden. In press.
Neri, F.& Saitta, L. (1993). Exploiting sample selection and ordering to speed-up learning. Proc. AAAI Symposium on Training Issues in Incremental Learning (Stanford, CA), pp. 54–69.
Newell, A. (1990). Unified theories of cognition, Cambridge, MA: Harvard University Press.
Nordhausen, B. & Pazzani, P. (1993). An integrated framework for empirical discovery. Machine Learning, 12, 17–47.
Ohlsson, S. (1987). Truth versus appropriateness: Relating declarative to procedural knowledge. In D. Klahr, P. Langley & R. Neches (Eds.), Production system models of learning and development, Cambridge, MA: MIT Press.
Ohlsson, S. (1996). Learning to do and learning to understand: A lesson and a challenge for cognitive modeling. In P. Reiman & H. Spada (Eds.), Learning in Humans and Machine (pp. 37–62) Pergamon Press, Oxford.
Pazzani, M. (1991). “Influence of prior knowledge in concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory, and Cognition, 17, 416–432.
Piaget, J. (1974). Understanding causality, New York, NY: Norton & Co.
Rumelhart, D. E. (1989). Toward a microstructural account of human reasoning, In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 299–312) New York, NY: Cambridge University Press.
Rumelhart, D. E. & Norman, D. A. (1977). Accretion, tuning and restructuring: Three modes of learning. In J.W. Cotton & R. L. Klatzky (Eds.), Semantic factors in cognition, Hillsdale, NJ: Erlbaum.
Sage, S. & Langley, P. (1983). Modeling cognitive development on the balance scale task. Proc. 8th Int. Joint Conf. on Artificial Intelligence (pp. 94–96) Karlsruhe, Germany.
Saitta, L. et al. (1995). Knowledge representation changes in humans and machines. In P. Reimann & H. Spada (Eds.), Learning in Humans and Machines: Towards an Interdisciplinary learning science, (pp. 109–129) Oxford, UK: Elsevier Publ. Co.
Saitta, L., Botta, M.,& Neri, F. (1993). Multistrategy learning and theory revision. Machine learning, 11, 153–172.
Salmon, W. (1989). Four decades of scientific explanation, Minneapolis, MN: University of Minnesota Press.
Samarapungavan, A. & Wiers, R.W. (1997). Children's thoughts on the origin of species: A study of explanatory coherence. Cognitive Science, 21, 147–177.
Schmidt, W. C. & Ling, C. X. (1996). A decision-tree model of balance scale development. Machine Learning, 24, 203–230.
Shultz, T. R., Mareschal, D., & Schmidt, W. (1994). Modeling cognitive developemnt on balance scale phenomena. Machine Learning, 16, 57–86.
Simon, T., Newell, A., & Klahr, D. (1991). A computational account of children's learning about number conservation, In D. H. Fisher, M. J. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning (pp. 423–462) San Francisco, CA: Morgan Kaufmann.
Sison, R., Numao, M., & Shimura, M. (1999). Multistrategy detection and discovery of novice programmer errors. Machine Learning, this issue.
Sleeman, D., Hirsh, H., Ellery, I., & Kim, I. (1990). Extending domain theories: two case studies in student modeling. Machine Learning, 5, 11–37.
Tiberghien, A. (1985). “Heat and temperature”, The development of ideas with teaching. In R. Driver, E. Guesne, & A. Tiberghien (Eds.), Children's ideas in science (pp. 67–84) Milton Keynes, UK: Open university press.
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, & H. F. Friedrich (Eds.), Learning and instruction. European research in an international context, Volume 2.1, Oxford, UK: Pergamon Press, pp. 631–648.
Tiberghien, A. (1994). Modelling as a basis for analysing teaching-learning situations. Learning and Instruction, 4, 71–87.
VanLehn, K. (1990). Mind Bugs: The origins of procedural misconceptions. Cambridge, MA: MIT Press.
VanLehn, K. & Jones, R. (1993a). Learning by explaining examples to oneself: A Computational model. In S. Chipman & A. L. Meyrowitz (Eds.), Foundations of knowledge acquisition: cognitive models of complex learning, Boston, MA: Kluwer.
VanLehn, K. & Jones, R. (1993b). Learning by explaining examples to Oneself: A computational model. In S. Chipman & A. L. Meyrowitz (Eds.), Foundations of knowledge acquisition: cognitive models of complex learning, Boston, MA: Kluwer.
Vosniadou, S. (1989). Analogical reasoning in knowledge acquisition. In S. Vosnadiou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 413–437) Cambridge, MA: Cambridge University Press.
Vosniadou, S. (1994). Capturing and modeling the process of conceptual change. Learning and instruction, 4, 45–69.
Vosniadou, S. (1995). A cognitive psychological approach to learning. In P. Reimann & H. Spada (Eds.), Learning in humans and machines: Towards an interdisciplinary learning science (pp. 23–36) Oxford, UK: Elsevier Publ. Co.
Vosniadou, S. & Brewer, W. F. (1992). Mental models of the earth: A study of conceptual change in childhood. Cognitive Psychology, 24, 535–585.
Vosniadou, S. & Brewer, W. F. (1994). Mental models of the day/night cycle. Cognitive Science, 18, 123–183.
Weil-Barais, A. & Vergnaud, G. (1990). Student's conceptions in physics and mathematics: biases and helps. In J. P. Caverni, J. M. Fabre, & M. Gonzalez (Eds.), Cognitive biases (pp. 69–84) North Holland, Elsevier Science Publ.
White, B. Y. & Frederiksen, J. R. (1987). Causal model progressions as a foundation for intelligent learning environments. Techn. Report No. 6686, BBN Laboratories, Cambridge, MA.
White, B. Y. & Frederiksen J. R. (1988). Explorations in understanding how physical systems work. Proc. of 10th Annual Meeting of the Cognitive Science Society, Lawrence Erlbaum, Hillsdale, NJ: Lawrence Erlbaum.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Neri, F. Multi Level Knowledge in Modeling Qualitative Physics Learning. Machine Learning 38, 181–211 (2000). https://doi.org/10.1023/A:1007642225146
Issue Date:
DOI: https://doi.org/10.1023/A:1007642225146