Title: Classification models of students' moods during an online self-assessment test

Authors: Christos N. Moridis, Anastasios A. Economides

Addresses: Information Systems Department, University of Macedonia, 156 Egnatia Avenue, Thessaloniki, 54006, Greece. ' Information Systems Department, University of Macedonia, 156 Egnatia Avenue, Thessaloniki, 54006, Greece

Abstract: A student|s emotional state is crucial during learning. When a student is in a very negative mood, learning is unlikely to occur. On the other hand, a too-positive mood can also impair learning. Thus a key issue for instructional technology is recognising the student|s mood, so as to be able to provide appropriate feedback. This paper introduces student|s mood models during an online self-assessment test. Two models were evaluated using data emanating from experiments with 153 high school students from three different regions of Greece. The results confirm the models| ability to estimate a student|s mood.

Keywords: affective computing; affective learning; computer-based testing; mood recognition; self-assessment tests; student moods; student emotions; emotional states; instructional technology; online self-assessment; Greece.

DOI: 10.1504/IJKL.2009.024546

International Journal of Knowledge and Learning, 2009 Vol.5 No.1, pp.50 - 61

Published online: 09 Apr 2009 *

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