Design of a classroom effect analysis system based on multimodal data fusion in smart classrooms

This study proposes a classroom effect analysis system based on multimodal data fusion, aiming to optimize the teaching effect evaluation method in smart classrooms. Through the collaborative collection and processing of multimodal data such as video, audio and sensors, a unified feature fusion framework is designed, and an analysis model with efficient feature extraction and joint representation capabilities is constructed. Experimental results show that the fusion model is significantly better than the single modality model in core indicators such as accuracy, precision, recall and F1 score, with an improvement of more than 15%, verifying the effectiveness of multimodal fusion in complex teaching scenarios. The real-time feedback and analysis functions of the system demonstrate its potential application value in classroom emotion recognition, student participation evaluation and teaching interaction optimization, providing important support for the realization of data-driven smart education.