A Study of Recurrent Education Support Methods Based on Online Learning Data

2022 Fiscal Year Final Research Report

• Project/Area Number: 18K02927
• 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: National Institute of Informatics
• Principal Investigator: Furukawa Masako 国立情報学研究所, 情報社会相関研究系, 助教 (20617287)
• Co-Investigator(Kenkyū-buntansha): 山地 一禎 国立情報学研究所, コンテンツ科学研究系, 教授 (50373379) ; 畑 耕治郎 大手前大学, 現代社会学部, 教授 (50460986)
• Project Period (FY): 2018-04-01 – 2023-03-31
• Keywords: オンライン学習 / リカレント教育 / 学習履歴データ / ラーニングアナリティクス

Outline of Final Research Achievements

This study aims to analyze the differences in learning behavior between students who have completed and withdrawn from the online learning history data accumulated at correspondence universities, and to link the findings on learning behavior patterns of students who have completed to effective learning support for other students, mainly for working students. The purpose of this study is to analyze the differences in learning behavior between students who have completed and those who have withdrawn from the university, and to link the findings on the learning behavior patterns of completed students to effective learning support for other students. Specifically, we developed a system to analyze and visualize learning history data, and extracted the characteristics of learning behavior patterns of all courses taken from the time of entrance to graduation, as well as the characteristics of learning behavior patterns of students who completed and withdrew from the same courses.

Free Research Field

教育工学

Academic Significance and Societal Importance of the Research Achievements

通信制大学においては、通学制に比べると修了率が低いという問題点があり、どのように遠隔学習を継続させるかについて課題が残っている。オンライン学習履歴データを蓄積している通信制大学におけるリカレント教育の支援システムを開発し、オンライン学習履歴データの解析を行うことによって、学生支援を効果的に行うことや、学習支援においては予め効率良い学習戦略をアドバイスできるとともに、様々な学習行動パタンに基づいて適切なアドバイスができることが期待できる。