Research on the Live Teaching Effect Based on Multimodal Data Fusion

Abstract

In the intelligent education era, multimodal data fusion analysis becomes a trend and a key method to address issues in education and teaching. As live teaching grows rapidly, the analysis of teaching effect is facing challenges of dynamic data collection, as well as multimodal data fusion analysis. Based on analyzing the connotation of multimodal data as well as research status-quo of live teaching, and with the assistance of EEG identification and facial expression identification among other technologies, the article carried out the analysis and experiment on live teaching performance based on multimodal data fusion, and discussed the role that multimodal data fusion played in the analysis of live teaching performance with respects to knowledge retention, students satisfaction, sense of presence, and interaction, etc., and eventually came with an optimization strategy for lives teaching activities with the support of multimodal data fusion and analysis. The research concluded that multimodal data fusion analysis enabled the all-round multidimensional recording of the live teaching process, brought about more comprehensive performance analysis of different teaching periods, and provided data support to tutoring.