Relating Machine Estimates of Students' Learning Goals to Learning Outcomes: A DBN Approach

Authors:
Carole R. Beal;Lei Qu
Affiliations:
Information Sciences Institute-USC, 4676 Admiralty Way, Marina del Rey CA 90292 USA, cbeal@isi.edu;Information Sciences Institute-USC, 4676 Admiralty Way, Marina del Rey CA 90292 USA, cbeal@isi.edu
Venue:
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Year:
2007

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Abstract

Students' actions while working with a tuoring system were used to generate estimates of learning goals, specifically, the goal of learning by using multimedia help resources, and the goal of learning through independent problem solving. A Dynamic Bayesian Network (DBN) model was trained with interface action and inter-action interval latency data from 115 high school students, and then tested with action data from an independent sample of 135 students. Estimates of learning goals generated by the model predicted student performance on a post-test of math achievement, whereas pre-test performance did not.