{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T04:47:58Z","timestamp":1792903678630,"version":"4.5.0"},"reference-count":39,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Province Higher Education Scientific Research Project","award":["JGJX24D0470"],"award-info":[{"award-number":["JGJX24D0470"]}]},{"name":"Jilin Province Science and Technology Development Plan Project","award":["YDZJ202201ZYTS549"],"award-info":[{"award-number":["YDZJ202201ZYTS549"]}]},{"name":"Changchun Science and Technology Development Plan Project","award":["21ZGM30"],"award-info":[{"award-number":["21ZGM30"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Student classroom behavior recognition is a core research direction in intelligent education systems. Real-time analysis of students\u2019 learning states and behavioral features through classroom monitoring provides quantitative support for teaching evaluation, classroom management, and personalized instruction, offering significant value for data-driven educational decision-making. To address the issues of low detection accuracy and severe occlusion in classroom behavior detection, this article proposes an improved YOLOv11n-based algorithm named YOLOv11-GLIDE. The model introduces a Channel Prior Convolutional Attention (CPCA) mechanism to integrate global and local feature information, enhancing feature extraction and detection performance. A scale-based dynamic loss (SD Loss) is designed to adaptively adjust the loss weights according to object scale, improving regression stability and detection accuracy. In addition, Sparse Depthwise Convolution (SPD-Conv) replaces traditional down-sampling to reduce fine-grained feature loss and computational cost. Experimental results on the SCB-Dataset3 demonstrate that YOLOv11-GLIDE achieves an excellent balance between accuracy and lightweight design. Compared with the baseline YOLOv11n, mAP@0.5 and mAP@0.5-0.95 increase by 2.5% and 7.6%, while Parameters and GFLOPS are reduced by 9.4% and 11.1%, respectively. The detection speed reaches 127.9 FPS, meeting the practical requirements of embedded classroom monitoring systems for accurate and efficient student behavior recognition.<\/jats:p>","DOI":"10.3390\/s25226972","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T16:46:22Z","timestamp":1763138782000},"page":"6972","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["YOLOv11-GLIDE: An Improved YOLOv11n Student Behavior Detection Algorithm Based on Scale-Based Dynamic Loss and Channel Prior Convolutional Attention"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2815-0978","authenticated-orcid":false,"given":"Haiyan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guiyuan","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kejing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Che","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caihua","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Changchun University, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sheng, X., Li, S., and Chan, S. 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