抜粋
The estimation of learners' mental states during the interaction between teachers and learners is a very important problem in improving the quality of teaching and learning. In this experimental study, we developed a deep learning neural network (DLNN) system that extracted the relationships between a learner's mental states and a teacher's utterances plus the learner's physiological information. The learner's physiological information consisted of the NIRS signals, the EEG signals, respiration intensity, skin conductance, and pulse volume. The learner's mental states were elicited through the learner's introspective reports using the Achievement Emotions Questionnaire (AEQ). According to the AEQ, the learner's mental states were divided into nine categories: Enjoy, Hope, Pride, Anger, Anxiety, Shame, Hopelessness, Boredom, and Others. In a simulation, the DLNN system exhibited the ability to estimate the learner's mental states from the learner's physiological information with high accuracy.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings |
| 編集者 | Ahmad Fauzi Mohd Ayub, Antonija Mitrovic, Jie-Chi Yang, Su Luan Wong, Wenli Chen |
| 出版者 | Asia-Pacific Society for Computers in Education |
| ページ | 56-61 |
| ページ数 | 6 |
| ISBN（印刷物） | 9789869401265 |
| 出版物ステータス | Published - 2017 1 1 |
| イベント | 25th International Conference on Computers in Education, ICCE 2017 - Christchurch, New Zealand 継続期間: 2017 12 4 → 2017 12 8 |
Other
| Other | 25th International Conference on Computers in Education, ICCE 2017 |
|---|---|
| 国 | New Zealand |
| 市 | Christchurch |
| 期間 | 17/12/4 → 17/12/8 |
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science Applications
- Information Systems
- Hardware and Architecture
- Education