Real-Time Analysis and Intervention of Classroom Behavior Using Multi-Modal Fusion and Spatiotemporal Context

Analyzing classroom engagement is essential for developing effective smart learning environments. Conventional methods often face challenges in achieving reliable identification of individual students, accurately recognizing their behavioral states, and providing timely support. This paper presents a multimodal sensing and supportive feedback system built upon an end–edge–cloud collaborative architecture. By integrating RFID-based seat association, fingerprint verification, and computer vision-based activity analysis, the system establishes a reliable link between student identity and observed activities. Key computational tasks, including activity recognition, spatiotemporal context matching, and rule-based assessment, are executed locally on edge nodes. This enables low-latency, privacy-conscious feedback delivered via Bluetooth, effectively avoiding delays associated with cloud processing. Experimental results indicate that the proposed system significantly enhances both activity recognition accuracy and identity–behavior association reliability in typical classroom scenarios while substantially reducing the average feedback latency compared to traditional approaches.