@article{4356,
  author = {Fan Wang},
  title = {Prediction Study of Vocational College Students’ English- Speaking Proficiency based on Decision Tree Algorithm},
  journal = {Journal of Information Technology Review},
  year = {2025},
  volume = {16},
  number = {1},
  doi = {https://doi.org/10.6025/jitr/2025/16/1/11-18},
  url = {https://www.dline.info/jitr/fulltext/v16n1/jitrv16n1_2.pdf},
  abstract = {With the continuous advancement of modern technology, various information technologies have gradually
matured and been applied in education. The design of English-speaking teaching in vocational colleges has
been a hot topic of concern for many researchers, and the actual situation of students learning English speaking
is also a major concern for educators. Up-to-date educational methods and diverse teaching approaches have
revitalised English-speaking classrooms in ordinary colleges. This paper uses the decision tree algorithm in
deep learning to analyse vocational college students’ English-speaking proficiency in-depth and establish a
predictive system for speaking learning effectiveness. Data analysis evaluates students’ English-speaking
abilities and levels, measuring changes in English-speaking effectiveness and providing reliable factors for
actual learning situations. The association algorithm is used to optimize the decision tree predictive
performance, predicting the English-speaking proficiency of vocational college students and providing
assistance for subsequent teaching. The research shows that the decision tree algorithm achieves high accuracy
in the statistical and predictive analysis of vocational college students’ English-speaking proficiency, which
greatly promotes the optimization of teaching strategies and methods.},
}