Detecting Learning Moment-by-Moment

Issue title: Best of ITS 2010

Article type: Research Article

Authors: Baker, Ryan S.J.D. | Goldstein, Adam B. | Heffernan, Neil T.

Affiliations: Department of Social Science and Policy Studies, Worcester Polytechnic Institute 100 Institute Road, Worcester MA 01609, USA. [email protected] | Department of Computer Science, Worcester Polytechnic Institute 100 Institute Road, Worcester MA 01609, USA. [email protected], [email protected]

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.

Keywords: Educational data mining, Bayesian knowledge tracing, student modeling, intelligent tutoring systems

DOI: 10.3233/JAI-2011-015

Journal: International Journal of Artificial Intelligence in Education, vol. 21, no. 1-2, pp. 5-25, 2011