オオノ　スミオ   Ohno, Sumio   大野　澄雄    所属   コンピュータサイエンス学部 コンピュータサイエンス学科    職種   教授
言語種別	日本語
発行・発表の年月	2026/01
形態種別	学術論文
査読	査読あり
標題	An Early-Warning Educational System with Small Samples and Across Academic Year Based on Combination of Transformer and XGBoost
執筆形態	共著
掲載誌名	J. Adv. Comput. Intell. Intell. Inform
掲載区分	国外
出版社・発行元	Fuji Technology Press
巻・号・頁	30(1),232-245頁
著者・共著者	X. Tan, J. She, S. Chen, S. Ohno, and H. Kameda
概要	Japan’s declining birthrate has been leading to an increasing distribution of academic abilities among university students. To ensure the successful completion of studies, it is crucial to identify students at-risk of academic failure at an early stage and provide them with the necessary support. In conventional teaching systems, this task is highly dependent on teaching experience. Recently developed early-warning systems (EWSs), which are based on the data mining of learning management systems, are built in post-hoc models and lack sufficient generalizability to new academic years. In this study, we present an EWS that combines data augmentation with a transfer learning-enhanced classification model. Through the use of multiple sub-modules, we construct a deep-learning classification model that enhances the generalizability of the EWS. A comparison between a conventional predictive classification model and the presented model shows that our model achieved the optimal overall performance. The stability of this fine-tuned model is verified by the hold-out method. Our EWS is purposefully addressed for difficulties in real-world teaching environments (that is, year-to-year sample domain shifts and small sample sizes); thus, it is robustly adaptable to diverse teaching environments. Teachers can use the recommendations made by the EWS to implement next steps in academic interventions, improve learning strategies, and help students succeed in their studies. All data collection uses non-identifiable information and protects privacy.