Real-time monitoring of students’ learning states is essential for improving engagement assessment and personalized intervention in online education. However, existing approaches often rely on computationally intensive facial expression analysis, gaze estimation, or multimodal behavior recognition, limiting their deployment in resource-constrained learning environments. To address these challenges, this study proposes a lightweight learning behavior monitoring framework based on drowsiness detection and screen-distance estimation.
An improved object detection model, termed YOLO26-P2-CBAM, is developed by integrating a high-resolution P2 detection head, the Convolutional Block Attention Module (CBAM), and the Wise-IoU (WIoU) loss function into the YOLO26 architecture. The proposed model enhances small-target perception and improves robustness under complex learning conditions. Based on this model, a browser–server collaborative monitoring system is implemented to perform real-time yawning detection and eye-to-screen distance estimation using webcam video streams.
In addition, parallel processing and batch inference strategies are introduced to improve computational efficiency in multi-user scenarios. Experiments were conducted on the public SCB-dataset and two self-constructed datasets for yawning detection and screen-distance estimation. The proposed model achieved an [email protected] of 63.2% on the SCB-dataset, outperforming the baseline YOLO26 by 4.2% points, while also achieving the highest Precision (62.2%) and F1-score (60.9%) among all evaluated methods, indicating a better balance between detection accuracy and target coverage.
On the self-constructed yawning dataset, it achieved an [email protected] of 86.8%, while screen-distance estimation yielded a mean absolute error of 2.12 cm. The average inference time was approximately 12.5 ms per frame, satisfying real-time application requirements. These results demonstrate that the proposed framework provides an effective and deployable solution for intelligent learning-state monitoring in online education.
This research was funded in part by Doctoral Research Project under Grant BKY202406, in part by the Langfang Municipal Science and Technology Bureau Project under Grant 2021011076, and by Teaching Research and Reform Project of North China Institute of Aerospace Engineering under Grand JY2025050. School of Computer Science, School of Materials Engineering, North China Institute of Aerospace Engineering, No. 133 Aimin East Road, Langfang, 065000, China Lijuan Diao, Wei Wu, Haozhe Lang, Jichao Li & Haibo Zhao The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Extract — continue reading at the source.
