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

Stamper, John C.

Koedinger, Kenneth R.

Date

2011

In publication

Artificial Intelligence in Education

Editor

Biswas, Gautam

Bull, Susan

Kay, Judy

Mitrovic, Antonija

Series

Lecture Notes in Computer Science

Pages

353-360

Publisher

Springer

Resource type

en Research/Scholarly Media

Resource status/form

en Published Text

Scholarship genre

en Empirical

Keywords

cognitive modeling

data mining

machine learning

Language

en

ISBN

978-3-642-21869-9

DOI

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

URL

Official Publisher's Webpage (Springer Link)

Full-text PDF Shared by Author (ResearchGate)

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

Place

Berlin, Heidelberg

Item sets

Handbook Chapter 20 Citations

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