Design and analysis of teaching early warning system based on multimodal data in an intelligent learning environment.

In online teaching environments, the lack of direct emotional interaction between teachers and students poses challenges for teachers to consciously and effectively manage their emotional expressions. The design and implementation of an early warning system for teaching provide a novel approach to intelligent evaluation and improvement of online education. This study focuses on segmenting different emotional segments and recognizing emotions in instructional videos. An efficient long-video emotional transition point search algorithm is proposed for segmenting video emotional segments. Leveraging the fact that teachers tend to maintain a neutral emotional state for significant portions of their teaching, a neutral emotional segment filtering algorithm based on facial features has been designed. A multimodal emotional recognition model is proposed for emotional recognition in instructional videos. It begins with preprocessing the raw speech and facial image features, employing a semi-supervised iterative feature normalization algorithm to eliminate individual teacher differences while preserving inherent differences between different emotions. A deep learning-based multimodal emotional recognition model for teacher instructional videos is introduced, incorporating an attention mechanism to automatically assign weights for feature-level modal fusion, providing users with accurate emotional classification. Finally, a teaching early warning system is implemented based on these algorithms.