Estimation of English Proficiency by Eye Gaze and EEG Analysis in English Reading Using Self-Organizing Maps

• Yako Takuya

  Department of Electronic Control Engineering,National Institute of Technology, Nagaoka College

• Shimoda Akira

  EM Special Section, Union Tool Co.

• Yako Ryota

  Graduate School of Electrical, Electronics and Information Engineering, Nagaoka University of Technology

• Wada Masaki

  Technological Support Center for Education and Research, National Institute of Technology, Nagaoka College

• Tsuchida Yasuko

  Division of General Education, National Institute of Technology, Nagaoka College

• Toyama Shigehiro

  Department of Electronic Control Engineering,National Institute of Technology, Nagaoka College

Bibliographic Information

Other Title

• 自己組織化マップを用いた英文読解時の視線・脳波解析による英語能力の推定

Abstract

With the globalization of the field of activity, it is an ongoing challenge to improve the English language skills of engineers and researchers. In this study, we propose a method for estimating English language ability using self-organizing maps with the gaze and EEG while reading English. 26 participants with different TOEIC L&R Test scores were used as the samples, and the validity was tested with 8 new participants. The participants read English texts on a PC display for one minute and rested for one minute with the eyes closed, meanwhile their gaze and EEG were recorded. In terms of gaze information, we analyzed pauses, regressions, reading speed, pupil diameter, and blinks during English reading. For the EEG, we focused on the frequency bands classified as the α wave, analyzed them with the frequency spectrum area. The characteristics of each cluster on the self-organizing map were analyzed by focusing on the listening score and reading score of the TOEIC L&R Test as the major strengths and weaknesses in English language proficiency. It was concluded that it is possible to estimate the strengths and weaknesses from the clusters classified on the self-organizing map and to suggest learning methods to improve the scores.

Journal

• The Transactions of Human Interface Society

  The Transactions of Human Interface Society 23 (4), 397-406, 2021-11-25

  Human Interface Society

Keywords

• Self-Organizing map
• Eye tracking
• EEG
• Bio-signal measurement
• English proficiency

Details

• CRID: 1390571647791341184
• NII Article ID: 130008120501
• DOI: 10.11184/his.23.4_397
• ISSN: 21868271
• ISSN: 13447262
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed