I learn from you, you learn from me: How to make iList learn from students

Authors:
Davide Fossati;Barbara Di Eugenio;Stellan Ohlsson;Christopher Brown;Lin Chen;David Cosejo
Affiliations:
Department of Computer Science, University of Illinois at Chicago;Department of Computer Science, University of Illinois at Chicago;Department of Psychology, University of Illinois at Chicago;Department of Computer Science, United States Naval Academy;Department of Computer Science, University of Illinois at Chicago;Department of Psychology, University of Illinois at Chicago
Venue:
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Year:
2009

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Abstract

We developed a new model for iList, our system that helps students learn linked list. The model is automatically extracted from past student data, and allows iList to track students' problem-solving behavior in order to provide targeted feedback. We evaluated the new model both intrinsically and extrinsically. We show that the model can match most student actions after a relatively small sequence of observations, and that iList can effectively use the new student tracker to provide feedback and help students learn.