{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T18:04:12Z","timestamp":1779300252979,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Monitoring students' behavior and engagement in real time is essential to improve learning outcomes and reduce academic risk. As a result of their reliance on static academic records and manual observations, traditional early warning systems often fail to identify behavioral markers predictive of declining involvement. BEACON-AI is a real-time deep learning platform proposed in this work for multimodal student behavior analysis and academic risk prediction. The system utilizes physiological, behavioral, and environmental data. These data are obtained from 30 days of wearable Internet of Things sensor records on Kaggle. These recordings include facial expressions, posture, heart rate, movement, and classroom context. To capture temporal and contextual patterns, the data preprocessing included normalization, one-hot encoding, and segmentation into sliding time-series windows, among other steps. BEACON-AI uses a CNN-BiLSTM-Attention hybrid model to forecast attention, engagement, and inactivity at multiple levels. With accuracy rates of 94.2% for attention detection, 92.5% for engagement categorization, and over 85% for early disengagement prediction, the system outperformed baseline models primarily used in academic settings. To demonstrate a high level of dependability in identifying behavioral changes, task-wise accuracy, recall, and F1 Scores were specifically measured. It is possible to comprehend the results of the attention mechanism since it highlights the most critical behavioral contributions. The overall value of combining multimodal behavioral and physiological data for educational interventions is demonstrated by BEACON-AI, which provides real-time, scalable behavioral monitoring and proactive academic early warning.<\/jats:p>","DOI":"10.31449\/inf.v50i10.10917","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:55Z","timestamp":1773832375000},"source":"Crossref","is-referenced-by-count":1,"title":["BEACON-AI: A CNN- BiLSTM -Attention Framework for Real-Time Multimodal Student Behavior Analysis and Academic Early Warning"],"prefix":"10.31449","volume":"50","author":[{"given":"Xinguo","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10917\/6623","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10917\/6623","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:56Z","timestamp":1773832376000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10917"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i10.10917","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}