Research on intelligent assessment of teaching quality in high-density classrooms based on improved WM-YOLO

Purpose This paper addresses challenges in high-density higher education classrooms, including severe occlusion and concealed disciplinary behaviors. It aims to propose the WM-YOLO network and a vision-based quality evaluation system for precise micro-behavior detection and macro-quality assessment. Design/methodology/approach We propose an improved WM-YOLO network that integrates Weighted Convolution (wConv2d) and a Multi-scale Attention Network (MANet). wConv2d suppresses occlusion noise, while MANet improves small-target detection. To validate this architecture, the research utilizes a self-constructed High-Density Classroom Behavior dataset (avg. 51 students/frame). Furthermore, the study redefines the Effective Heads-up Rate (HuR, denoted R_eff) and Negative Engagement Index (I_neg) to analyze classroom state dynamics. Findings Experiments show that WM-YOLO achieves an mAP@50 of 92.3%, outperforming baselines by 1.4% with 27% lower computation. Empirical analysis reveals a “Negative Scissors Difference” (heads-down exceeding heads-up) during non-teaching periods, validating the model’s ability to distinguish classroom states. Practical implications The proposed evaluation model effectively supports higher education administration. By identifying abnormal fluctuations in I_neg and the “Scissors Difference” phenomenon, administrators can automatically filter out non-teaching durations, enabling automated attendance tracking and precise teaching quality warnings, thus promoting a shift from “experience-based management” to “data-driven governance”. Originality/value This paper innovatively applies wConv2d and MANet to high-density scenes. It establishes a quantitative link between micro-behaviors and macro-evaluation via the “Scissors Difference” principle, offering a new paradigm for smart education.