An Intelligent Evaluation Method of Information Course Teaching Effect Based on Image Analysis

Abstract

Due to its own limitations, the traditional teaching quality evaluation method has been unable to adapt to the development of information-based curriculum teaching. Therefore, the establishment of a scientific and intelligent teaching effect evaluation method will help to improve the teaching quality of college teachers. To solve the above problems, a student fatigue state evaluation method based on the quantum particle swarm optimization artificial neural network is proposed. Firstly, face detection is realized by adding three Haar-like feature blocks and improving the AdaBoost algorithm of a weak classifier connection. Secondly, in order to effectively improve the image imbalance, the MSR algorithm is used to enhance the face data image, which is effectively suitable for network training. Then, by readjusting the connection mode, the DenseNet is improved to fully reflect the local detail feature information of the low level. Finally, quantum particle swarm optimization (QPSO) is used to optimize the DenseNet structure, which makes the optimization of network structure more automatic and solves the uncertainty of manual selection. The experimental results show that the proposed method has a good detection effect and prove the effectiveness and correctness of the proposed method.