Article: Enhancing English teaching effectiveness in vocational colleges: a data-driven approach using machine learning and adaptive learning models Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.24 pp.1 - 14 Abstract: This study explores the integration of machine learning (ML) and adaptive learning technologies in enhancing English teaching effectiveness in vocational colleges. A comprehensive dataset was collected from student interactions and feedback to evaluate engagement levels and learning outcomes. Text-based features such as TF-IDF, POS tagging, and Word2Vec embeddings were extracted and analysed using traditional ML and deep learning models including SVM, decision tree, naive Bayes, LSTM, and RNN. The hybrid CNN+ViT model achieved the highest classification accuracy of 92.7%, demonstrating the effectiveness of integrating machine learning for improving English teaching strategies. These findings suggest a data-driven path for optimising personalised instruction in English language education. Inderscience Publishers - linking academia, business and industry through research

Title: Enhancing English teaching effectiveness in vocational colleges: a data-driven approach using machine learning and adaptive learning models

Authors: Xiao Wang

Addresses: College of Basic Education, Chongqing Industry and Trade Polytechnic, Fuling, 408000, Chongqing, China

Abstract: This study explores the integration of machine learning (ML) and adaptive learning technologies in enhancing English teaching effectiveness in vocational colleges. A comprehensive dataset was collected from student interactions and feedback to evaluate engagement levels and learning outcomes. Text-based features such as TF-IDF, POS tagging, and Word2Vec embeddings were extracted and analysed using traditional ML and deep learning models including SVM, decision tree, naive Bayes, LSTM, and RNN. The hybrid CNN+ViT model achieved the highest classification accuracy of 92.7%, demonstrating the effectiveness of integrating machine learning for improving English teaching strategies. These findings suggest a data-driven path for optimising personalised instruction in English language education.

Keywords: English teaching; vocational colleges; machine learning; adaptive learning; data-driven education.

DOI: 10.1504/IJICT.2025.147133

International Journal of Information and Communication Technology, 2025 Vol.26 No.24, pp.1 - 14

Received: 19 Mar 2025
Accepted: 06 May 2025
Published online: 10 Jul 2025 *

Title: Enhancing English teaching effectiveness in vocational colleges: a data-driven approach using machine learning and adaptive learning models

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