Analysis and Visualization of Class Attitudes and Understanding using Real-time Learning Analytics

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K03078
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 09070:Educational technology-related
• Research Institution: Kanagawa Institute of Technology
• Principal Investigator: Tanaka Tetsuo 神奈川工科大学, 情報学部, 教授 (90727984)
• Co-Investigator(Kenkyū-buntansha): 松本 一教 神奈川工科大学, 情報学部, 教授 (40350673)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: ラーニングアナリティクス / 授業支援 / 穴埋め教材 / 学修行動ログ / プログラミング演習

Outline of Final Research Achievements

We have developed a fill-in workbook that supports lecture-style classes and a programming exercise system that supports exercise-style classes, with the aim of allowing teachers to ascertain the attitudes and understanding of students during classes.
The fill-in workbook forces students to interact with the system, and it is possible to collect learning behavior logs, including inactive students. We applied it to actual classes and confirmed that the purpose was achieved.
The programming exercise system accumulates operation logs during programming and presents the progress of the entire class and the progress of each student to the teacher. We applied it to an actual class and confirmed that it was possible to ascertain the students' coding status.

Free Research Field

教育工学

Academic Significance and Societal Importance of the Research Achievements

学校教育において，アクティブとは言えない学生についても授業に集中しているのか，ただ教材の該当ページを開いているだけなのか，多くの学生がどこで躓いているのかなどをリアルタイムに把握し，教員が状況に合わせて適切なアクションを取ることができるようになる．また学術的には，ラーニングアナリティクスにおける分析対象をアクティブではない学生の授業中の詳細データにまで広げることになり，本分野の発展に寄与する．