International Conference on Artificial Intelligence in Education

AIED 2011: Artificial Intelligence in Education pp 353-360

Human-Machine Student Model Discovery and Improvement Using DataShop

  • John C. Stamper
  • Kenneth R. Koedinger
Conference paper

DOI: 10.1007/978-3-642-21869-9_46

Volume 6738 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Stamper J.C., Koedinger K.R. (2011) Human-Machine Student Model Discovery and Improvement Using DataShop. In: Biswas G., Bull S., Kay J., Mitrovic A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, vol 6738. Springer, Berlin, Heidelberg

Abstract

We show how data visualization and modeling tools can be used with human input to improve student models. We present strategies for discovering potential flaws in existing student models and use them to identify improvements in a Geometry model. A key discovery was that the student model should distinguish problem steps requiring problem decomposition planning and execution from problem steps requiring just execution of problem decomposition plans. This change to the student model better fits student data not only in the original data set, but also in two other data sets from different sets of students. We also show how such student model changes can be used to modify a tutoring system, not only in terms of the usual student model effects on the tutor’s problem selection, but also in driving the creation of new problems and hint messages.

Keywords

data mining machine learning cognitive modeling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • John C. Stamper
    • 1
  • Kenneth R. Koedinger
    • 1
  1. 1.Human-Computer Interaction InstituteCarnegie Mellon UniversityUSA