期刊
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I
卷 12748, 期 -, 页码 241-254出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-78292-4_20
关键词
Co-occurring emotions; Eye-tracking; Logs; Classification
类别
资金
- National Science Foundation [DRL-1431552]
- Natural Sciences and Engineering Research Council [22R01881]
This paper explores the co-occurrence of emotions in the educational context, especially boredom+frustration and curiosity+anxiety, using MetaTutor ITS as a test-bed. The study demonstrates that these emotions can be predicted from data better than baseline using eye-tracking and interaction data, suggesting a promising direction for developing affect-aware ITS.
Emotions in Intelligent Tutoring Systems (ITS) are often modeled as single affective states, however there is evidence that emotions co-occur during learning, with implications for affect-aware ITS that need to have a comprehensive understanding of a student's affective state to react accordingly. In this paper we broaden the evidence that emotions co-occur in an educational context, and present a first attempt to predict these co-occurrences from data, using the MetaTutor ITS as a test-bed. We show that boredom+frustration, as well as curiosity+anxiety, frequently co-occur in MetaTutor, and that we can predict when these emotions co-occur significantly better than a baseline using eye-tracking and interaction data. These findings provide a first step toward building affect-aware ITS that can adapt to these complex co-occurring affective states.
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