2022 Fiscal Year Final Research Report
Learning Support and Classification of Learning Behaviors based on Bursts in Learning Logs
| Project/Area Number |
19K12273
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 62030:Learning support system-related
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| Research Institution | Kumamoto University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
松葉 龍一 東京工科大学, 先進教育支援センター, 教授 (40336227)
平岡 斉士 熊本大学, 教授システム学研究センター, 准教授 (80456772)
合田 美子 熊本大学, 教授システム学研究センター, 准教授 (00433706)
鈴木 雄清 大分大学, 高等教育開発センター, 准教授 (00333253)
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| Project Period (FY) |
2019-04-01 – 2023-03-31
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| Keywords | ラーニングアナリティクス / 先延ばし行動 / 学習履歴 / 特徴量エンジニアリング / ナレッジトレーシング / eラーニング / 高等教育 |
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| Outline of Final Research Achievements |
This study aimed to analyze the patterns of achievement and engagement variations among learners using the learning management system (LMS) logs, with the goal of classifying learners into different learning behavior types. The classified learner groups were then provided with learning support based on self-regulated learning strategies, with the expectation of achieving high learning outcomes. By analyzing the learning logs of the weekly exam, the learners were divided into two clusters: procrastinating learners and habitual learners. The results revealed that the performance of habitual learners was superior to that of procrastinating learners. In terms of assignment submission deadlines, an attempt was made to provide reminders to learners who exhibited procrastination tendencies. To predict learners' procrastination behavior, feature vectors were constructed based on specific elements, and the use of these feature vectors improved the accuracy of predictions.
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| Free Research Field |
教育工学
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| Academic Significance and Societal Importance of the Research Achievements |
課題を提出する締切やオンラインクイズを合格する締切など，学習活動で締切が設定されることは多く，学習者はついつい締切を目指して行動し，ときに先延ばしにより締切に間に合わないという結果を招く．この研究は先延ばし行動を早めに検知して学習者を励まして学習へと向かわせることを目指した．残念ながら高い精度では検知できなかったが，学習者の行動を数値化する試みは精度を高める足掛かりとなると考えている．
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