Research field (2):
Learning support systems
, Information theory
Research keywords (3):
Learning Analytics
, Natural Computing
, Theory of Computation
Research theme for competitive and other funds (6):
2022 - 2026 Construction and Evaluation of a High-Density Learning Analytics Infrastructure for Data-Driven Education
2022 - 2026 Development and Evaluation of Learning Analytics Platform based on Learning Improvement Model
2021 - 2024 Development and evaluation of learning analytics dashboard for the decision-making support of learning behavior improvement
2016 - 2021 化学反応オートマトンにおける決定性計算の研究
2013 - 2015 化学反応オートマトンの解析と応用に関する研究
2012 - 2013 化学反応オートマトンの研究
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Papers (77):
Abdul Berr, Sukrit Leelaluk, Cheng Tang, Li Chen, Fumiya Okubo, Atsushi Shimada. Educational Data Analysis using Generative AI. LAK Workshops. 2024. 47-55
Ikkei Igawa, Yuta Taniguchi, Tsubasa Minematsu, Fumiya Okubo, Atsushi Shimada. Investigating Programming Performance Predictability from Embedding Vectors of Coding Behaviors. 31st International Conference on Computers in Education, ICCE 2023 - Proceedings. 2023. 1. 487-489
Ryusuke Murata, Fumiya Okubo, Tsubasa Minematsu, Yuta Taniguchi, Atsushi Shimada. Recurrent Neural Network-FitNets: Improving Early Prediction of Student Performanceby Time-Series Knowledge Distillation. Journal of Educational Computing Research. 2023. 61. 3. 639-670
Fumiya Okubo, Tetsuya Shiino, Tsubasa Minematsu, Yuta Taniguchi, Atsushi Shimada. Adaptive Learning Support System Based on Automatic Recommendation of Personalized Review Materials. IEEE Transactions on Learning Technologies. 2023. 16. 1. 92-105
Erwin D. López Z., Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Atsushi Shimada. LECTOR: An attention-based model to quantify e-book lecture slides and topics relationships. EDM. 2023