Deep Convolutional Neural Network and Weighted Bayesian Model for Evaluation of College Foreign Language Multimedia Teaching

Abstract

In colleges and universities, teaching quality evaluation is an integral part of the teaching management process. Many factors influence it, and the relationship between its evaluation index and instructional quality is complicated, abstract, and nonlinear. However, existing evaluation methods and models have flaws such as excessive subjectivity and randomness, difficulty determining the weight of indicators, easy over-fitting, slow convergence speed, and limited computing power, to name a few. Furthermore, the evaluation index system focuses primarily on teaching attitude, material, and methods, rarely taking into account preparation prior to teaching or the teaching situation throughout the teaching process, resulting in an incomplete evaluation. As a result, learning how to construct a model for objectively, truly, thoroughly, and accurately assessing the teaching quality of colleges and universities is beneficial not only to improving teaching quality but also to promoting scientific decision-making in education. This paper develops a teaching assessment model using a deep convolutional neural network and the weighted Naive Bayes algorithm. Based on the degree of influence of different characteristics on the assessment outcomes, a method to estimate the weight of each evaluation characteristic by employing the related probability of class attributes is proposed, and the corresponding weight is assigned for each evaluation index, resulting in a classification model ideal for teaching assessment that promotes standardization and intelligibility.