Affiliations: [a]
Huaxin College Of Hebei GEO University, Shijiazhuang, Hebei, China
| [b] Department of Tourism, College of Preschool Education, Hebei Normal University, Shijiazhuang, Hebei, China
Correspondence:
[*]
Corresponding author. Xiaohui Du, Hebei Normal University, E-mail: chenyuanyuan09@sina.com.
Abstract: The diagnostic evaluation model of English learning is difficult to judge the subjective factors in student learning, so some diagnostic evaluation models of English learning are difficult to apply to English learning practice. In order to improve the effect of English learning, based on machine learning technology, this study combines the needs of English evaluation to build a diagnostic evaluation model of English learning based on machine learning. Moreover, this study compares the methods of random forest, Bayesian network, decision tree, perceptron, K-nearest neighbor and multi-model fusion, and selects the best algorithm for diagnostic analysis. The diagnostic evaluation model of English studies constructed in this paper mainly evaluates and judges the errors in students’ English learning. In addition, this study validates the methods proposed in this study through controlled experiments. The research results show that the method proposed in this study has a certain effect.
Keywords: Machine learning, English learning, diagnosis; evaluation model
