{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T06:43:17Z","timestamp":1759473797847,"version":"build-2065373602"},"reference-count":42,"publisher":"World Scientific Pub Co Pte Ltd","issue":"18","funder":[{"name":"Chongqing Talent","award":["cstc2024ycjh-bgzxm0082"],"award-info":[{"award-number":["cstc2024ycjh-bgzxm0082"]}]},{"name":"Central University Operating Expenses","award":["2024CDJGF-044"],"award-info":[{"award-number":["2024CDJGF-044"]}]},{"name":"Chongqing new YC project","award":["CSTB2024YCJH-KYXM0126"],"award-info":[{"award-number":["CSTB2024YCJH-KYXM0126"]}]},{"name":"Basic Scientific Research Operating Expenses Fund of Central universities","award":["2025CDJZKZCQ-11"],"award-info":[{"award-number":["2025CDJZKZCQ-11"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p> With the development of educational informatization, multimedia teaching has become an essential means of higher education and has penetrated various disciplines. Traditional teaching methods have difficulty detecting and effectively intervening in students\u2019 abnormal behavior in time, which affects students\u2019 academic performance and future development. Recently, deep learning algorithms have been employed for abnormal behavior detection. However, existing studies focus on prediction accuracy and lack solutions and dynamic analysis. To further promote the development of students\u2019 abnormal behavior detection technology and improve teaching quality, this paper proposes a time series-based framework for students\u2019 abnormal behavior detection and learning behavior recommendation, which can dynamically identify and monitor students\u2019 abnormal behavior. Specifically, LSTM and the transformer are used in this paper to capture the time dependence of students\u2019 learning behaviors for binary classification prediction. Variational autoencoders (VAEs) are used as generative models to learn the distribution of learning behaviors of low-risk students and recommend learning behaviors for high-risk students. The experimental results show that the framework proposed in this paper can realize real-time monitoring and personalized intervention of students\u2019 learning behavior, which is helpful for reforming teaching and improving teaching quality. <\/jats:p>","DOI":"10.1142\/s0218126625503359","type":"journal-article","created":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T01:36:58Z","timestamp":1745631418000},"source":"Crossref","is-referenced-by-count":0,"title":["Real-Time Time Series-Based Behavior Anomaly Detection and Strategic Innovation in Edge Teaching Environments"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6296-7773","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Automation, Shanghai University, Shanghai 200444, P.\u00a0R.\u00a0China"},{"name":"Shanghai Aerospace Equipment Manufacturing General Factory Co. 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