Application of Real-Time Behavior Tracking Algorithm Combined with Yolov8 in Student Behavior Detection

 

Xin Bai1, Madhavi Devaraj1,*, Zhe Zhang1

1School of Information Technology, Mapua University, Manila 1002, Philippines

Emails: Xin.Bai@gmail.com; madhavidevaraj@gmail.com; Zhe.Zhang@gmail.com

Text Box: Abstract 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.

Received: March 27, 2025 Revised: June 28, 2025 Accepted: August 30, 2025

Keywords: Yolov8; Real-time detection; Behavioral tracking; Student behavior