AI-Powered Teaching Behavior Analysis by Using 3D-MobileNet and Statistical Optimization

摘要

Artificial intelligent technology can realize multi-angle analysis and feedback of teaching process. This paper provides an innovative auxiliary for classroom teaching evaluation and fills in the lack of teacher behavior analysis in AI application. Firstly, a 3D-MobileNet framework is proposed for behavior recognition, which can process time-domain information for the video through layered training. Next, we design a comprehensive model by using both the analytic hierarchy process and entropy weight method (AHP-EW) to output the quantitative results of the teaching evaluation in three dimensions. This model combines the subjective and objective weights through a statistical optimization strategy to improve the credibility. Finally, we test our model on a 45-min teaching video, and compare it with the existing model in various aspects, proving that our method is highly feasible and competitive.