Skip to main content

Part of the book series: NATO ASI Series ((NATO ASI F,volume 107))

  • 138 Accesses

Abstract

The student model in four Intelligent Tutoring Systemsdealing with the learning of electricity is discussed with respect to cognitive modeling. The issue is to know if the student model has taken into account the results of research in physics education. Different kinds of modeling have been proposed in order to explain the student errors: mental models, sequential reasoning and electrical circuit prototypes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Van Lehn, K.: Student Modeling. In Poison, M.C. & Richardson, J.J. (Eds): Foundations of Intelligent Tutoring Systems. Hillsdale, N.J.: Lawrence Erlbaum Associates (1988).

    Google Scholar 

  2. Duit, R., Jung, W. & von Rhöneck, C. (Eds): Aspects of Understanding Electricity.. Kiel: IPN-Arbeitsberichte (1985).

    Google Scholar 

  3. Shipstone, D.M., von Rhöneck, C., Jung, W., Kärrqvist,C., Dupin, J.J., Joshua, S. & Licht, P.: A study of students’ understanding of electricity in five European countries. International Journal of Science Education, 10 (3), 303–316, (1988).

    Article  Google Scholar 

  4. Shipstone, D.M.: On childrens’ use of conceptual models in reasoning about current electricity. In Duit, R., Jung, W. & von Rhöneck, C. (Eds): Aspects of understanding electricity. pp 73–93. Kiel: IPNArbeitsberichte (1985).

    Google Scholar 

  5. Closset J.L.: Le raisonnement séquentiel en électrocinétique [The sequential reasoning in basic electricity]. Doctoral dissertation, University of Paris 7 (1983).

    Google Scholar 

  6. Caillot, M.: Problem representations and problem solving procedures in electricity. In: Duit,R., Jung,W. & von Rhöneck, C. (Eds): Aspects of understanding electricity, pp 139–152. Kiel: IPN Arbeitsberichte (1985).

    Google Scholar 

  7. Caillot, M.: Circuits électriques: schématisation et résolution de problèmes [Electrical circuits: diagrams and problem solving]. Technologies, Idéologies, Pratiques, VII (2), 59–83 (1988).

    Google Scholar 

  8. Cauzunolle-Marmèche, E. & Mathieu, J.: Concevoir des systèmes d’EIAO qui reposent sur une modélisation du fonctionnement de l’élève [Designing ICAI systems based on student modeling]. Technologies, Idéologies, Pratiques, VII (2), 85–115 (1988).

    Google Scholar 

  9. Rosch, E.: On the internal structure of perceptual and semantic categories. In Moore T.E. (Ed): Cognitive development and the acquisition of language, pp. 111–144. New York: Academic Press (1973).

    Google Scholar 

  10. Subrahmanian, E., Talukdar, S. & Mullen, W.: Dr. Thevenin: an intelligent tutoring system for electrical circuits. Technical report. Engineering Design Research Center, Carnegie-Mellon University, Pittsburgh, PA(1986).

    Google Scholar 

  11. Lesgold, A.M., Bonar, J.G., Ivill, J.M. & Bowen, A.: An intelligent tutoring system for electronic troubleshooting: DC-circuit understanding. Technical report. Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA(1987).

    Google Scholar 

  12. Bonar, J.G.: Bite-Sized Intelligent Tutoring. ITSG Newsletter 85–3. Learning Research and Development Center, University of Pittsburgh, Pittsburgh, PA (1985).

    Google Scholar 

  13. White, B.Y. & Frederiksen, J.R.: Qualitative models and intelligent learning environments. In Lawler, R. & Yazdani, M. Eds): AI and Education, pp. 281–305. Norwood, NJ: Ablex Publishing Co. (1987).

    Google Scholar 

  14. White, B.Y. & Frederiksen, J.R.: Progressions of qualitative models as a foundationfor intelligent learning environments. Technical report No. 6277; BBN Laboratories Inc., Cambridge, MA (1986).

    Google Scholar 

  15. Paliès, O., Caillot, M., Cauzinelle-Marmèche, E., Laurière, J.L. & Mathieu, J.: Student modelling by a knowledge-based system. Computational Intelligence, 2, 99–107, (1986).

    Article  Google Scholar 

  16. Paliès, O.: Méta-Connaissances pour la modélisation de l’élève. Contribution au diagnostic cognitif par système expert [Metaknowledge for student modeling. Contribution to cognitive diagnosis by an expert system]. Doctoral dissertation, Pierre-etMarie-Curie University, Paris, (1988).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Caillot, M. (1993). Learning Electricity and Cognitive Modeling. In: Ferguson, D.L. (eds) Advanced Educational Technologies for Mathematics and Science. NATO ASI Series, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02938-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-02938-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08152-1

  • Online ISBN: 978-3-662-02938-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics