English Learning Behaviour Pattern Mining and Personalized Teaching Strategies Based on Big Data Analysis

Authors

  • Yingying Xiao School of Education, Jingzhou University, Jingzhou 434000, Hubei, China

Abstract

With the development of information technology, data-driven decision-making in education is becoming increasingly important, especially in the personalization of English language teaching. In this study, large-scale English learning behavior data were deeply mined through a set of wellestablished analysis processes, using quantitative methods such as cluster analysis and association rule analysis. It was found that the careful delineation and parsing of students’ behavioral patterns revealed individual differences in terms of learning habits, preferences, and challenges faced by students when learning English. The experimental results show that the experimental group that implemented personalized teaching strategies demonstrated more significant improvement in the learning behaviors (e.g., online learning hours, number of interactions, task completion, etc.) and academic performance (e.g., test scores, homework grading) than those of the control group that were exposed traditional teaching modes. The case analyses of specific cases also demonstrated that the personalized teaching strategy designed according to students’ individual characteristics can effectively improve the learning outcomes of students.

Keywords: big data analytics, English learning, behavioral pattern mining, personalized instruction

Cite As

Y. Xiao, "English Learning Behaviour Pattern Mining and Personalized Teaching Strategies Based
on Big Data Analysis", Engineering Intelligent Systems, vol. 32 no. 6, pp. 647-657, 2024.




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Published

2024-11-01

Issue

Vol. 32 No. 6 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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