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| Content Provider | ACM Digital Library |
|---|---|
| Author | Xu, Zhenhua Woodruff, Earl |
| Abstract | Emotions form an integral part of our cognitive function. Past research has demonstrated conclusive associations between emotions and learning achievement [7, 26, 27]. This paper used a person-centered approach to explore students' (N = 65) facial behavior, emotions, learner traits and learning. An automatic facial expression recognition system was used to detect both middle school and university students' real-time facial movements while they learned scientific tasks in a 3D narrative video game. The results indicated a strong statistical relationship between three specific facial movements (i.e., outer brow raising, lip tightening and lip pressing), student self-regulatory learning strategy and learning performance. Outer brow raising (AU2) had strong predictive power when a student is confronted with obstacles and does not know how to proceed. Both lip tightening and pressing (AU23 and AU24) were predictive when a student engaged in a task that requires a deep level of incoming information processing and short memory activation. The findings also suggested a correlational relationship between student self-regulatory learning strategy use and neutral state. It is hoped that this study will provide empirical evidence for helping us develop a deeper understanding of the relationship between facial behavior and complex learning especially in educational games. |
| Starting Page | 439 |
| Ending Page | 443 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450348706 |
| DOI | 10.1145/3027385.3027432 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-03-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Educational video games Facial expression recognition Complex learning Scientific reasoning Game-based learning Learner traits Emotion Affect |
| Content Type | Text |
| Resource Type | Article |
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