{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:11:53Z","timestamp":1781532713528,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62207027"],"award-info":[{"award-number":["62207027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62177024"],"award-info":[{"award-number":["62177024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62207027"],"award-info":[{"award-number":["62207027"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62177024"],"award-info":[{"award-number":["62177024"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Philosophy and Social Sciences Planning Project","award":["23NDJC112YB"],"award-info":[{"award-number":["23NDJC112YB"]}]},{"name":"Zhejiang Provincial Education Science Planning Project","award":["2023SCG369"],"award-info":[{"award-number":["2023SCG369"]}]},{"name":"Jinhua Municipal Federation of Social Sciences Project","award":["YB2025112"],"award-info":[{"award-number":["YB2025112"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The rapid development of intelligent learning guidance systems has created a favorable environment for personalized learning. By accurately predicting students\u2019 future performance, education can be tailored and teaching strategies optimized. However, traditional prediction algorithms seldom account for highly imbalanced datasets in basic education, overlook temporal factors, and lack further interpretability of the prediction results. To address these shortcomings, we propose Temporal Variational Autoencoder-Generative Adversarial Network (TVAE-GAN), a temporal variational autoencoder-generative adversarial network model aimed at providing early warnings for high-risk students in basic education, with in-depth interpretability analysis of the prediction results to suit the unique context of basic education. TVAE-GAN extracts features from real samples and introduces a Long Short-Term Memory (LSTM) network to capture dynamic features in time series, helping the model better understand temporal dependencies in the data, remember the sequential causal information of students\u2019 online learning, and achieve better data generation performance. Using these features, the generative model generates new samples, and the discriminator model evaluates their quality, producing outputs that closely resemble real samples through training. The effectiveness of the TVAE-GAN model is validated on a collected online basic education dataset while also advancing the timing of interventions in predictions. The performance differences between the proposed method and classic resampling methods, as well as their impact in the educational field, are analyzed, highlighting that misclassification increases teacher workload and affects students\u2019 emotions. Key influencing factors are identified using a decision-tree surrogate model, providing teachers with multidimensional references for academic assessment.<\/jats:p>","DOI":"10.3390\/info17040356","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:49:44Z","timestamp":1775641784000},"page":"356","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TVAE-GAN: A Generative Model for Providing Early Warnings to High-Risk Students in Basic Education and Its Explanation"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9315-5417","authenticated-orcid":false,"given":"Chao","family":"Duan","sequence":"first","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7734-3730","authenticated-orcid":false,"given":"Wenlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Geomatics, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5041-6093","authenticated-orcid":false,"given":"Qionghao","family":"Huang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1186\/s40537-024-00918-5","article-title":"Enhancing academic performance prediction with temporal graph networks for massive open online courses","volume":"11","author":"Huang","year":"2024","journal-title":"J. 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