Intelligent Assessment of students’ Classroom Interest Based on Multidimensional Visual Perception

Students’ classroom interest is a critical factor influencing learning outcomes, and its intelligent detection has become an important issue in smart education. Traditional assessment relies on teacher observation or post-class questionnaires, which are time-consuming, labor-intensive, and subjective, and which lack real-time feedback. Some studies infer interest using physiological signals from wearable EEG or ECG devices, but these methods may interfere with normal learning. The study focuses on the typical classroom teaching scene and proposes a classroom interest assessment method, which is designed to achieve non-intrusive, contactless, real-time feedback using computer vision techniques. The proposed method extracts three types of visual cues from students during classroom learning: behavior, attention, and facial emotion. Each dimension is quantified to generate a corresponding score, and the three scores are integrated to assess students’ classroom interest levels. Experiments on a self-constructed classroom dataset show that the proposed model can effectively recognize classroom interest levels.