An Evaluation Model of Higher Vocational English Teaching Effect Based on Particle Swarm Optimization and Support Vector Machine

Vocational English teaching is an important educational task in vocational education. In order to achieve better teaching results, it is necessary to conduct real-time evaluation of English teaching. This paper proposes an evaluation model of English teaching effect in higher vocational colleges based on Particle swarm optimization and support vector machine. First, this paper uses Particle swarm optimization algorithm to assign weights to multiple factors in the evaluation model to ensure that the weights of evaluation factors are more accurate. Afterwards, a support vector machine algorithm was used to construct an English teaching dataset model. By analyzing and modeling the features in the data, the optimal value of teaching effectiveness was predicted, and corresponding recommendations were made.

Finally, this article verifies through experiments that the results provided by the evaluation model are consistent with the actual results. Models can play an important role in comprehensively analyzing teaching data, accurately evaluating teaching effectiveness, and optimizing teaching strategies. This model can be widely applied in the field of vocational English teaching, and is expected to provide better teaching tools and technical support for teachers, improving students’ learning effectiveness and level. In a word, the evaluation model of higher vocational English teaching effect based on Particle swarm optimization and support vector machine discussed in this paper has made full use of algorithm technology and data analysis means to obtain a more accurate and efficient teaching model in the teaching field, so as to improve the teaching effect and provide new ideas and exploration methods for the education reform in English education and other similar education fields.