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Dynamic Approaches to Modeling Student Affect and its Changing Role in Learning and Performance

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9146)

Abstract

We investigate the relation between students’ affect, learning and performance in the context of the ASSISTments online math tutoring system. Moment-by-moment estimates of students’ affective states derived from a series of affect detectors accompany each student response within the tutoring system. By applying a series modified factorial hidden Markov models that account for students’ affective state at the time of the given response and comparing the models’ performance to the standard Bayesian Knowledge Tracing (BKT) approach, we evaluate the impact of affect on estimates of students’ guess and slip behavior. The investigation suggests a model based approach to improving student models in the context of online tutoring systems.

Keywords

Knowledge tracing Emotion Affect Learning Performance Hidden markov models Factorial hidden markov models Automated tutoring systems 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Graduate School of EducationUniversity of CaliforniaBerkeleyUSA
  2. 2.School of InformationUniversity of CaliforniaBerkeleyUSA

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