Detecting learning moment-by-moment

Authors:
Ryan S. J. D. Baker;Adam B. Goldstein;Neil T. Heffernan
Affiliations:
Department of Social Science and Policy Studies, Worcester Polytechnic Institute, Worcester, MA;Department of Computer Science, Worcester Polytechnic Institute, Worcester MA;Department of Computer Science, Worcester Polytechnic Institute, Worcester MA
Venue:
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Year:
2011

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Abstract

Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a specific problem step (instead of at the next or previous problem step). We use this model to analyze which KCs are learned gradually, and which are learned in "eureka" moments. We also discuss potential ways that this model could be used to improve the effectiveness of cognitive mastery learning.