Temporal Data Mining for Educational Applications

  • Carole R. Beal
  • Paul R. Cohen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5351)


Intelligent tutoring systems (ITSs) acquire rich data about studentsÖ behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    (Retrieved July 8, 2006), http://www.ed.gov/nclb/landing.jhtml
  2. 2.
    Beal, C.R., Qu, L., Lee, H.: Classifying learner engagement through integration of multiple data sources. In: Proceedings of the 21st National Conference on Artificial Intelligence. AAAI Press, Menlo Park (2006)Google Scholar
  3. 3.
    Koedinger, K.R., Corbett, A.T., Ritter, S., Shapiro, L.J.: Carnegie Learnings Cognitive Tutor: Summary of research results. Carnegie Learning, Pittsburgh (2000)Google Scholar
  4. 4.
    Baker, R.S., Corbett, A.T., Koedinger, K.R., Roll, I.: Detecting when students game the system, across tutor subjects and classroom cohorts. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 220–224. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Beck, J.: Engagement tracing: Using response times to model student disengagement. In: Looi, C., McCalla, G., Bredeweg, B., Breuker, J. (eds.) Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology, pp. 88–95. IOS Press, Amsterdam (2005)Google Scholar
  6. 6.
    Stevens, R., Johnson, D., Soller, A.: Probabilities and prediction: Modeling the development of scientific problem solving skills. Cell Biology Education 4, 42–57 (2005)CrossRefGoogle Scholar
  7. 7.
    Sutton, D., Cohen, P.R.: Very predictive Ngrams for space-limited probabilistic models. In: Pfenning, F., et al. (eds.) Advances in intelligent data analysis V, pp. 134–142. Springer, Berlin (2003)Google Scholar
  8. 8.
    Beal, C.R., Mitra, S., Cohen, P.R.: Modeling learning patterns of students with a tutoring system using Hidden Markov Models. In: Proceedings of the 13th International Conference on Artificial Intelligence in Education (AIED), Rey, CA (2006) (July 2007)Google Scholar
  9. 9.
    Ramoni, M., Sebastiani, P., Cohen, P.R.: Bayesian clustering by dynamics. Machine Learning 47, 91–121 (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Beal, C.R., Cohen, P.R.: Computational methods for evaluating student and group learning histories in intelligent tutoring systems. In: Looi, C., McCalla, G., Bredeweg, B., Breuker, J. (eds.) Artificial Intelligence in Education: Supporting learning through intelligent and socially-informed technology, pp. 80–87. IOS Press, Amsterdam (2005)Google Scholar
  11. 11.
    Cohen, P.R.: Empirical methods for artificial intelligence. MIT Press, Cambridge (1995)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Carole R. Beal
    • 1
  • Paul R. Cohen
    • 1
  1. 1.University of ArizonaUSA

Personalised recommendations