Development and Validation of Discipline-Based Learning Analytics in Undergraduate Education

2023 Fiscal Year Final Research Report

• Project/Area Number: 20K03118
• 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: Obihiro University of Agriculture and Veterinary Medicine
• Principal Investigator: SAITO Jun 帯広畜産大学, 畜産学部, 准教授 (90757668)
• Project Period (FY): 2020-04-01 – 2024-03-31
• Keywords: Learning Analytics / DBER / LMS

Outline of Final Research Achievements

This study aimed to develop quantitative and qualitative middle-scale learning analytics in a practical way and to validate its effectiveness in undergraduate education. As an outcome, we developed a method for efficiently acquiring and storing learning data on a learning management system (LMS) and found that the analysis of the data would enable quantitative evaluation of learning and its outcomes that were unavailable with traditional evaluation. As a result, it was quantitatively clarified that learning processes and affective domains, such as regularly planned learning, positive expectations for learning and proactive involvement (i.e. agency), are linked to learning outcomes and efficacy.

Free Research Field

理論物理学

Academic Significance and Societal Importance of the Research Achievements

本研究の成果により，学習者の詳細な学習状況・履歴は，特殊・高額な機器・装置やソフトウェア等を一切要することなく容易に実装でき，かつデータ取得のために学習へ干渉することなくリアルタイムで取得・蓄積可能であることが明らかとなった。また，その分析によって，学習プロセスおよびその学習成果との関係を定量的に評価可能であることが明らかとなった。展望として，より公正な学習評価や即時・客観的なフィードバックの提供，リフレクションの促進，プロセスの自動評価等による教育・学習活動の効率化・省力化等が期待される。