Modeling the Development of Problem Solving Skills in Chemistry with a Web-Based Tutor

  • Ron Stevens
  • Amy Soller
  • Melanie Cooper
  • Marcia Sprang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3220)


This research describes a probabilistic approach for developing predictive models of how students learn problem-solving skills in general qualitative chemistry. The goal is to use these models to apply active, real-time interventions when the learning appears less than optimal. We first use self-organizing artificial neural networks to identify the most common student strategies on the online tasks, and then apply Hidden Markov Modeling to sequences of these strategies to model learning trajectories. We have found that: strategic learning trajectories, which are consistent with theories of competence development, can be modeled with a stochastic state transition paradigm; trajectories differ across gender, collaborative groups and student ability; and, these models can be used to accurately (>80%) predict future performances. While we modeled this approach in chemistry, it is applicable to many science domains where learning in a complex domain can be followed over time.


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  1. 1.
    Anderson, J.D.: Cognitive psychology and its implications. W.H. Freeman, San Francisco (1980)Google Scholar
  2. 2.
    Chi, M.T.H., Glaser, R., Farr, M.J. (eds.): The Nature of Expertise, pp. 129–152. Lawrence Erlbaum, Hillsdale (1988)Google Scholar
  3. 3.
    Chi, M.T.H., Bassok, M., Lewis, M.W., Reinmann, P., Glaser, R.: Self- Explanations: how students study and use examples in learning to solve problems. Cognitive Science 13, 145–182 (1989)CrossRefGoogle Scholar
  4. 4.
    VanLehn, K.: Cognitive Skill Acquisition. Annu. Rev. Psychol. 47, 513–539 (1996)CrossRefGoogle Scholar
  5. 5.
    Schunn, C.D., Anderson, J.R.: The generality/specificity of expertise in scientific reasoning. Cognitive Science (2002)Google Scholar
  6. 6.
    Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction 4, 253–278 (1995)CrossRefGoogle Scholar
  7. 7.
    Schunn, C.D., Lovett, M.C., Reder, L.M.: Awareness and working memory in strategy adaptivity. Memory & Cognition 29(2), 254–266 (2001)CrossRefGoogle Scholar
  8. 8.
    Haider, H., Frensch, P.A.: The role of information reduction in skill acquisition. Cognitive Psychology 30, 304–337 (1996)CrossRefGoogle Scholar
  9. 9.
    Alexander, P.: The development of expertise: the journey from acclimation to proficiency. Educational Researcher 32(8), 10–14 (2003)CrossRefGoogle Scholar
  10. 10.
    Stevens, R.H., Ikeda, J., Casillas, A., Palacio-Cayetano, J., Clyman, S.: Artificial neural network-based performance assessments. Computers in Human Behavior 15, 295–314 (1999)CrossRefGoogle Scholar
  11. 11.
    Underdahl, J., Palacio-Cayetano, J., Stevens, R.: Practice makes perfect: assessing and enhancing knowledge and problem-solving skills with IMMEX software. Learning and Leading with Technology 28, 26–31 (2001)Google Scholar
  12. 12.
    Lawson, A.E.: Science Teaching and the Development of Thinking. Wadsworth Publishing Company, Belmont (1995)Google Scholar
  13. 13.
    Olson, A., Loucks-Horsley, S. (eds.): Inquiry and the National Science Education Standards: A guide for teaching and learning. National Academy Press, Washington (2000)Google Scholar
  14. 14.
    Linacre, J.M.: WINSTEPS Rasch measurement computer program. Chicago. (2004)Google Scholar
  15. 15.
    Stevens, R.H., Najafi, K.: Artificial neural networks as adjuncts for assessing Medical students’ problem-solving performances on computer-based simulations. Computers and Biomedical Research 26(2), 172–187 (1993)CrossRefGoogle Scholar
  16. 16.
    Rumelhart, D.E., McClelland, J.L.: Parallel distributed processing: Explorations in the Microstructure of Cognition. Foundations, vol. 1. MIT Press, Cambridge (1986)Google Scholar
  17. 17.
    Stevens, R., Wang, P., Lopo, A.: Artificial neural networks can distinguish novice and expert strategies during complex problem solving. JAMIA 3(2), 131–138 (1996)Google Scholar
  18. 18.
    Casillas, A.M., Clyman, S.G., Fan, Y.V., Stevens, R.H.: Exploring alternative models of complex patient management with artificial neural networks. Advances in Health Sciences Education 1, 1–19 (1999)Google Scholar
  19. 19.
    Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. IEEE 77, 257–286 (1989)CrossRefGoogle Scholar
  20. 20.
    Kohonen, T.: Self Organizing Maps, 3rd extended edn. Springer, Heidelberg (2001)Google Scholar
  21. 21.
    Soller, A.: Understanding knowledge sharing breakdowns: A meeting of the quantitative and qualitative minds. Journal of Computer Assisted Learning (2004) (in press)Google Scholar
  22. 22.
    Soller, A., Lesgold, A.: A computational approach to analyzing online knowledge sharing interaction. In: Proceedings of Artificial Intelligence in Education, 2003, Australia, pp. 253–260 (2003)Google Scholar
  23. 23.
    Lesgold, A., Katz, S., Greenberg, L., Hughes, E., Eggan, G.: Extensions of intelligent tutoring paradigms to support collaborative learning. In: Dijkstra, S., Krammer, H., van Merrienboer, J. (eds.) Instructional Models in Computer-Based Learning Environments, pp. 291–311. Springer, Berlin (1992)Google Scholar
  24. 24.
    Lajoie, S.P.: Transitions and trajectories for studies of expertise. Educational Researcher 32, 21–25 (2003)CrossRefGoogle Scholar
  25. 25.
    Giordani, A., Soller, A.: Strategic Collaboration Support in a Web-based Scientific Inquiry Environment. In: European Conference on Artificial Intelligence, “Workshop on Artificial Intelligence in Computer Supported Collaborative Learning”, Valencia, Spain (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ron Stevens
    • 1
  • Amy Soller
    • 2
  • Melanie Cooper
    • 3
  • Marcia Sprang
    • 4
  1. 1.IMMEX ProjectUCLACulver City
  2. 2.ITC-IRSTPovo, TrentoItaly
  3. 3.Department of ChemistryClemson UniversityClemson
  4. 4.Placentia-Yorba Linda Unified School DistrictAnaheimUSA

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