Extraction of relationships between learners' physiological information and learners' mental states by machine learning

Yoshimasa Tawatsuji, Tatsuro Uno, Keita Okazaki, Siyuan Fang, Tatsunori Matsui

研究成果: Conference contribution

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 42017 12 8

Other

Other25th International Conference on Computers in Education, ICCE 2017
New Zealand
Christchurch
期間17/12/417/12/8

    フィンガープリント

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science Applications
  • Information Systems
  • Hardware and Architecture
  • Education

これを引用

Tawatsuji, Y., Uno, T., Okazaki, K., Fang, S., & Matsui, T. (2017). Extraction of relationships between learners' physiological information and learners' mental states by machine learning. : A. F. Mohd Ayub, A. Mitrovic, J-C. Yang, S. L. Wong, & W. Chen (版), Proceedings of the 25th International Conference on Computers in Education, ICCE 2017 - Main Conference Proceedings (pp. 56-61). Asia-Pacific Society for Computers in Education.