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Interactive learning log collection and analysis

Research Project

Project/Area Number 21K17864
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 62030:Learning support system-related
Research InstitutionKyushu University

Principal Investigator

Minematsu Tsubasa  九州大学, データ駆動イノベーション推進本部, 准教授 (00838914)

Project Period (FY) 2021-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2023: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2022: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Keywordsラーニングアナリティクス / 学習分析 / 機械学習 / 深層学習 / デジタル教科書 / 推薦システム / マルチモーダルデータ
Outline of Research at the Start

デジタル学習環境を通して,学習者の学習行動履歴を収集し,エビデンスベースで学習状況の把握が行われ始めている.学習状況の把握には,学習内容のメモや電子教科書の文章に残すマーカーといった学習者自身の考えを反映した積極的な行動の履歴が有用となるが,
(1)大多数の学習者は積極的に行動せず,有益な学習行動履歴が記録されにくい,(2)学習行動を促す効果的な指示が必要だが,常時行うことは不可能といった問題から,全学習者の詳細な学習状況の把握は困難な状況である.本研究では,システムと学習者のインタラクションを通して,自動的に学習行動履歴の収集分析の効率化を図る機構を開発する.

Outline of Final Research Achievements


We developed (1) a dashboard to promote e-textbook activities, (2) cheating detection using learner behavior analysis, (3) a feature representation learning method for highly expressive browsing logs, (4) an ensemble learning method for anomaly detection, and (5) the generation of answer explanations using LLMs. For (1), we developed a function to present information from e-textbooks and confirmed that interaction between teachers and learners occurred via the dashboard. In (2), (3) and (4), we worked on a wide range of topics, from the feature expression of learning behavior, cheating detection and anomaly detection methods with a view to application to learning analysis. For (5), we investigated LLM’s potential for recommendation generation, and obtained findings for dashboard improvements.

Academic Significance and Societal Importance of the Research Achievements

学習ログの収集および分析の研究は,ラーニングアナリティクス分野で国内外問わず行われているが,学習ツールを学習者が利用して学習ログを残すことが前提となっている.実運用では,学習ツール利用を含む講義計画を綿密に計画し,その利用促進を適宜行う必要があるが,その点に関して言及した研究はほとんどない.また,学習者とシステムの相互関係を利用した研究として,チュータリングシステムや学習ログの分析結果の提示により学習者によい行動変容を促す研究などがあるが,学習分析の可用性を高める目的の研究ではない.本研究の成果では,学習分析を効率化する学習ログ収集および効率的な学習ログ分析手法の提案を行った点で意義がある.

Report

(4 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Research-status Report
  • 2021 Research-status Report
  • Research Products

    (3 results)

All 2023 2022

All Presentation (3 results) (of which Int'l Joint Research: 3 results)

  • [Presentation] Contrastive Learning for Reading Behavior Embedding in E-book System2023

    • Author(s)
      Tsubasa Minematsu, Yuta Taniguchi and Atsushi Shimada
    • Organizer
      Artificial Intelligence in Education. AIED 2023
    • Related Report
      2023 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Background Subtraction Network Module Ensemble for Background Scene Adaptation2022

    • Author(s)
      Taiki Hamada, Tsubasa Minematsu, Atsushi Shimada, Fumiya Okubo, Yuta Taniguchi
    • Organizer
      International Conference on Advanced Video and Signal Based Surveillance (AVSS2022)
    • Related Report
      2022 Research-status Report
    • Int'l Joint Research
  • [Presentation] Can Learning Logs Be Useful Evidence in Cheating Analysis in Essay-type Questions?2022

    • Author(s)
      Tsubasa Minematsu, Atsushi Shimada
    • Organizer
      The 12th International Conference on Learning Analytics & Knowledge (LAK22)
    • Related Report
      2021 Research-status Report
    • Int'l Joint Research

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Published: 2021-04-28   Modified: 2025-01-30  

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