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date: 14 December 2019

Abstract and Keywords

This chapter is from the forthcoming The Oxford Handbook of Affective Computing edited by Rafael Calvo, Sidney K. D'Mello, Jonathan Gratch, and Arvid Kappas. In recent years, the essential role that affect plays during learning has received greater attention. Accordingly, there has been increasing interest in developing affect-sensitive learning systems that can infer student affect and respond appropriately to it. In many domains, researchers have leveraged physical sensors to make substantial progress in affect detection, but this approach can be challenging in educational settings, where physical sensors can be infeasible. As such, there has been increasing interest in developing detectors of student affect that operate solely on data from the interaction (within the user interface) between the student and the computer. This chapter reviews recent research in this area, discussing approaches used both for collecting training labels of affect and approaches for predicting those affect labels from features of the student interaction. The authors also discuss some of the methodological insights that are emerging from this area of research.

Keywords: Affect detector, educational data mining, features, interaction log, intelligent tutoring system, overfitting

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