Classification models of students' moods during an online self-assessment test. (10th April 2009)

• Record Type: Journal Article
• Authors: Moridis, Christos N.; Economides, Anastasios A.
• Is Part Of: International journal of knowledge and learning. Volume 5:Number 1(2009)
• Page Start: 50
• Page End: 61
• Publication Date: 2009-04-10
• Subjects: affective computing -- affective learning -- computer-based testing -- mood recognition -- self-assessment tests -- student moods -- student emotions -- emotional states -- instructional technology -- online self-assessment -- Greece; Knowledge and learning -- Periodicals; Knowledge management -- Periodicals; 306.4205
• Languages: English
• ISSNs: 1741-1009

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.