2021 Fiscal Year Final Research Report

Development of a self-study system that can feel the learner's emotion beyond time and space for language learning

Research Project

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Project/Area Number	19H01721
Research Category	Grant-in-Aid for Scientific Research (B)
Allocation Type	Single-year Grants
Section	一般
Review Section	Basic Section 09070:Educational technology-related
Research Institution	Shonan Institute of Technology
Principal Investigator	Umezawa Katsuyuki 湘南工科大学, 工学部, 教授 (20780282)
Co-Investigator(Kenkyū-buntansha)	平澤茂一早稲田大学, 理工学術院, 名誉教授 (30147946) 中澤真会津大学短期大学部, 産業情報学科, 教授 (40288014) 中野美知子早稲田大学, 教育・総合科学学術院, 名誉教授 (70148229) 小林学早稲田大学, データ科学センター, 教授 (80308204) 石井雄隆千葉大学, 教育学部, 准教授 (90756545)
Project Period (FY)	2019-04-01 – 2022-03-31
Keywords	eラーニング / 学習分析 / 言語学習 / 生体情報 / 脳波
Outline of Final Research Achievements	The purpose of this study is to develop and evaluate a self-study system equipped with an artificial teacher who senses the learner and gives advice, based on a unified framework for language learning. The term "sense the learner" means that the system side understands the learner's learning status. In this study, we developed a method and system for detecting learners' stumbling blocks and estimating their learning status, which are necessary for the development of a self-study system. We also conducted a demonstration experiment for learning English and programming languages using these methods and system. In addition, we investigated the relationship between English education and programming language education, and the use of these methods in education. Furthermore, we studied the possibility of substituting measurement instruments other than EEG for the dissemination of the results of this research.
Free Research Field	教育工学とセキュリティ
Academic Significance and Societal Importance of the Research Achievements	コロナ禍での新たな学習形態としてオンライン授業やハイブリッド授業なども必要に迫られて急激に広まった．しかし従来の自学自習システムではあらかじめ用意された学習コンテンツを使うだけであり，学習者一人ひとりの学習状態に応じた対応ができるものではなかった．多くの学習システムはあらかじめ作成した静的な学習コンテンツを学習者全員に均一に配布する方法がとられており，学習者の個性や学習状況に依存したきめ細かな対応はとられていない．本研究の個々の学習者の学習状態を把握して，その学習者に最適な学習コンテンツを提供できる自学自習システムの需要は極めて高い。