A Vision sensing-based automatic evaluation method for teaching effect based on deep residual network.

The automatic evaluation of the teaching effect has been a technical problem for many years. Because only video frames are available for it, and the information extraction from such dynamic scenes still remains challenging. In recent years, the progress of deep learning has boosted the application of computer vision in many areas, which can provide much insight into the above issue. As a consequence, this paper proposes a vision sensing-based automatic evaluation method for teaching effects based on deep residual network (DRN). The DRN is utilized to construct a backbone network for sensing from visual features such as attending status, taking notes, playing phones, looking outside, etc. The extracted visual features are further selected as the basis for the evaluation of the teaching effect. We have also collected some realistic course images to establish a real-world dataset for the performance assessment of the proposal. The proposed method is implemented on collected datasets via computer programming-based simulation experiments, so as to obtain accuracy assessment results as measurement. The obtained results show that the proposal can well perceive typical visual features from video frames of courses and realize automatic evaluation of the teaching effect.