Application of Decision Tree Algorithm Based on Data Mining in English Teaching Evaluation

Abstract

In order to bring new opportunities and challenges to teaching and learning English. This paper proposes a study of the use of a data tree-based decision tree algorithm in English language learning assessment. Teaching and learning through online English learning platforms using data mining technology to analyze student learning data, create relevant models, and explore the relationship between English exams and various elements is important for student learning and teacher teaching. Firstly, this paper introduces the function, process, and common machine learning models of data mining. Finally, by using data created by college English tutors and measurements to create and identify student images, through image users, you can learn about student behavior, learning behaviors, etc., provide a foundation after the design experiment, technology training technology in logistic regression model, wooden model making, and postfusion modeling of two models and analyze the factors affecting students’ passing the exam according to the prediction results. The results show that the decision tree model predicts that the score of each question type occurs 72 times, the score of students’ examination occurs 70 times, and the completion of homework occurs 70 times. Trees determined by this approach can have a profound impact on a wide range of indicators of academic excellence and provide a fundamental and useful basis for measuring future improvement.