Construction of College English teaching effect evaluation model based on artificial intelligence and output-oriented approach (Informatica, 2024)
In order to improve the accuracy and efficiency of teaching effect evaluation results, this paper proposes a model design method for college English teaching effect evaluation based on output-oriented method in artificial intelligence environment. The standard deviation transformation and range transformation are used to standardize the English teaching data, and the fuzzy similarity matrix is established. The data are preliminarily classified and clustered according to the results of data standardization; Support vector machine is used to classify English teaching data to solve the problem of data imbalance; This paper evaluates the effect of college English teaching based on POA theory, follows the construction principle of evaluation index, constructs the evaluation index system of college English teaching effect, establishes the teaching effect evaluation model based on fuzzy comprehensive evaluation method, and improves the traditional fuzzy evaluation model with the idea of minimum membership weighted average deviation method to obtain the weight of evaluation index, so as to realize the evaluation of college English teaching effect. The experimental results show that the output-oriented method can effectively improve the level of English teaching, and the evaluation efficiency of this method is higher and the evaluation results are more accurate.