Abstract
It is important to know the mental states of learners during the learning process to improve the effectiveness of teaching and learning. In this study, we first extracted the relationships between learners' mental states and teachers' speech acts, as well as learners' physiological information, by constructing a deep learning system. The physiological indexes were near infrared spectroscopy (NIRS), electroencephalography (EEG), respiration intensity, skin conductance, and pulse volume. Learners' mental states were divided into nine categories in accordance with the Achievement Emotions Questionnaire. In our experiment, the system achieved a high accuracy in predicting the learner's mental states from the teacher's speech acts and the learner's physiological information. A mock-up experiment was then conducted, which revealed that the system's interface was able to support teaching and learning in real time.
| Original language | English |
|---|---|
| Title of host publication | ICCE 2018 - 26th International Conference on Computers in Education, Main Conference Proceedings |
| Editors | Ma. Mercedes T. Rodrigo, Jie-Chi Yang, Lung-Hsiang Wong, Maiga Chang |
| Publisher | Asia-Pacific Society for Computers in Education |
| Pages | 107-109 |
| Number of pages | 3 |
| ISBN (Electronic) | 9789869401289 |
| Publication status | Published - 2018 Nov 24 |
| Event | 26th International Conference on Computers in Education, ICCE 2018 - Metro Manila, Philippines Duration: 2018 Nov 26 → 2018 Nov 30 |
Other
| Other | 26th International Conference on Computers in Education, ICCE 2018 |
|---|---|
| Country | Philippines |
| City | Metro Manila |
| Period | 18/11/26 → 18/11/30 |
Keywords
- Achievement emotions questionnaire
- Deep learning
- Emotion estimation
- Learning support
- Physiological information
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science Applications
- Education