Human-Machine Student Model Discovery and Improvement Using DataShop
Item
Title
Human-Machine Student Model Discovery and Improvement Using DataShop
Abstract/Description
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.
Author/creator
Date
In publication
Pages
353-360
Publisher
Springer
Resource type
Research/Scholarly Media
Resource status/form
Published Text
Scholarship genre
Empirical
ISBN
978-3-642-21869-9
Citation
Stamper, J. C., & Koedinger, K. R. (2011). Human-Machine Student Model Discovery and Improvement Using DataShop. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial Intelligence in Education (pp. 353–360). Springer. https://doi.org/10.1007/978-3-642-21869-9_46
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