| Project/Area Number |
20J15339
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 62030:Learning support system-related
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| Research Institution | National Institute of Information and Communications Technology |
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Principal Investigator |
GAN Wenbin 国立研究開発法人情報通信研究機構, ユニバーサルコミュニケーション研究所統合ビッグデータ研究センター, 研究員
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| Project Period (FY) |
2020-04-24 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 2021: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2020: ¥1,100,000 (Direct Cost: ¥1,100,000)
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| Keywords | Knowledge Tracing / Cognitive Diagnosis / Intelligent Tutoring / Performance Modeling / Item Response Theory / Learner Assessment / AI in Education / Education Data Mining / Knowledge Assessment |
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| Outline of Research at the Start |
This research aims at developing an AI-empowered intelligent tutoring system, which can automatically generate tailored remedial questions to remedy learners’ deficiency and provide step-by-step solutions and instructions based on learners’current knowledge levels obtained by using cognitive diagnosis assessment, and thus enhancing their e-learning experience and maximizing learning gains.
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| Outline of Annual Research Achievements |
This year I continue the work on learner's knowledge assessment (LKA). I have further explored the research of fine-grained assessment and interpretability. Improved on my previous work [BESC’20], I propose a novel model that can not only output the learners’ fine-grained knowledge states but also the item characteristics, enabling the interpretability. Extensive model analyses conducted from six perspectives on five real-world datasets validate its superiority. This work has been published in a top journal [Neurocomputing].
Another work solves the fundamental issues of data sparseness and information loss while improving the model performance. It has explored to incorporate the knowledge structure (KS) into the LKA to potentially resolve the above issues. This work automatically generates the KS from the learning logs and proposes a novel graph model with the attention mechanism. Extensive experiments show the effectiveness. This work has been published in a top journal [IJIS].
The above work stimulates a new idea of multimodal learning analysis. I have published a review paper about the empirical evidence on the usage of multimodal analysis to provide insights for smarter education. I also participated in a work published in [ICCE’21], in which a graph-based method is proposed for LKA.
I also finished my doctoral thesis, in which I summarize my PhD works. Overall, it proposes a general framework for dynamic LKA by integrating both learner and domain modeling. Based on this framework, it proposes three approaches, each addressing one specific issue in existing studies.
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| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和3年度が最終年度であるため、記入しない。
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