A Quantitative Study on Strategies for Enhancing English Classroom Interaction Based on Decision Tree Analysis

The quality of classroom interaction has received more and more attention as an important part of the teaching quality of English-specialized courses in colleges and universities. After completing the decision tree-based English classroom interaction analysis model based on the ID3 algorithm, this paper quantitatively analyzes the English classroom interaction rules and then designs the English classroom interaction enhancement strategies containing the three directions of teacher-teacher, teacher-student, and student-student on the basis of the derived rules and conducts a one-academic-year teaching experiment for English majors in a university in Guangzhou. It was found that the experimental class and the control class had the largest gap in two indicators: classroom discussion sessions (19.38% and 2.61%) and student manipulation techniques (9.07% and 0.41%). In the two classes at the beginning of the semester, the quality level scores of classroom interactions in both classes were below 30. In contrast, in the last two classes of the school year, the experimental class remained stable above 70 points until the end of classroom instruction. The control class remained below 30 points. After the beginning of the teaching experiment, the probability of students’ active speaking, group collaboration, or discussion in the experimental class increased rapidly. The probability of students’ active speaking stabilized at about 0.8 by the end of the 16th week. In contrast, the probability points of the control class were distributed more discretely in the course of the experiment, and the probability was basically below 0.4. In the experimental class, the English classroom interaction enhancement strategy proposed in this paper has yielded better results.