2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 434-446, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Xin Bai 1 * , Madhavi Devaraj 2 , Zhe Zhang 3
Doi: https://doi.org/10.54216/JISIoT.180230
In the intelligent teaching environment, it is indirect and difficult for teachers to capture learners’ learning attitudes and behaviors through digital learning behavior data provided by intelligent platforms. The purpose of this paper is to improve the precision of student behavior detection in teaching, and to provide teachers with a more reliable basis for making teaching plans. The Yolov8 algorithm is applied to student behavior recognition, and a bounding box loss function based on dynamic focusing mechanism is introduced to make a balance between samples with good regression quality and poor regression quality. Through experimental analysis, we can see that the real-time behavior tracking algorithm combined with Yolov8 proposed in this paper has a good application effect in student behavior detection. Moreover, it not only improves the precision of student behavior recognition, but also improves the stability of the algorithm, which is conducive to the effective development of subsequent smart teaching models.
Yolov8 , Real-time detection , Behavioral tracking , Student behavior
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