| Project/Area Number |
26540183
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Learning support system
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| Research Institution | Kyushu University |
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Principal Investigator |
MINE Tsunenori 九州大学, システム情報科学研究院, 准教授 (30243851)
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| Co-Investigator(Kenkyū-buntansha) |
廣川 佐千男 九州大学, 情報基盤研究開発センター, 教授 (40126785)
合田 和正 九州情報大学, 経営情報学部, 准教授 (50320396)
石岡 恒憲 独立行政法人大学入試センター, 研究開発部, 教授 (80311166)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2014: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | テキストマイニング / ラーニングアナリティクス / 機械学習 / コメントマイニング / 授業後報告文 / 成績推定 / 学習状況推定 / 授業後振り返り文 / 授業評価 / 最終成績推定 / 学習属性 / フィードバック / 学習意欲 / 理解度 / 学習状況報告文 / PCN法 |
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| Outline of Final Research Achievements |
We analyzed student free-style comments written by themselves just after every lesson and extract words related to student learning characteristics, learning performance and/or class assessment. We set five learning attributes:
effort, attitude, understanding, collaboration, findings and proposed novel methods to estimate student learning performance using the learning attributes. The methods use several machine learning methods such as SVM, Artificial Neural Network, Random Forest, LDA, pLSA, LSA, Word2Vec, Multi-instance Learning. We found there were high correlations between combination of the attributes and student grades. We also showed the effect of teacher interventions for improving the quality of student comments, understanding student learning situations and estimating student learning performance.
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