Evaluation of Multimedia Classroom Teaching Effectiveness Based on RS-BP Neural Network

Abstract

With the popularization of information technology, multimedia teaching has been widely used in universities as a new form of classroom teaching. In this paper, based on the classroom process, 12 evaluation indexes are initially obtained from three dimensions of “courseware, classroom teaching, and classroom effect,” which are reduced to 7 core indexes and evaluated comprehensively by using the rough set theory (RS), and the evaluation results are used as input data for simulation training of the BP neural network. The RS-BP neural evaluation model of multimedia classroom teaching effect (MCTE) is successfully trained, and finally five nonuniversities are selected for empirical research. The empirical study shows that this model has certain applicability when MCTE is such a nonlinear problem and can provide reference for the quality evaluation and improvement of multimedia teaching. The model in this study has certain practical value, but the index system is not comprehensive enough, the training data is insufficient, and the model maturity still needs further improvement.