Construction of English Wisdom Classroom Based on Educational Big Data Mining

Authors

  • Hui Gao School of International Education, Beijing University of Financial Technology, Beijing 101118, China
  • Xiaotian Wei School of International Education, Beijing University of Financial Technology, Beijing 101118, China
  • Xiaohui Tang School of Foreign Language, Shandong Jianzhu University, Jinan 250000, China

Abstract

The aim of this study is to explore the academic outcomes resulting from the implementation of an intelligent classroom based on educational big data mining in English teaching. Ninety-eight students at HS middle school were randomly divided into two groups: experimental and control. The experimental group were exposed to wisdom classroom teaching, while traditional teaching methods were used for the control group. The results showed that the experimental group performed significantly better than the control group in terms of English scores, online learning time, assignment submission frequency, class interaction frequency, and course satisfaction. Students in the experimental group showed higher learning motivation and self-efficacy. Moreover, the evaluation of technology use in the smart classroom also received positive feedback, indicating low frequency technical failure and high user-interface friendliness. These findings validate the effectiveness of the smart classroom in improving students’ learning outcomes, enhancing attitude to learning, and optimizing teaching-student interaction. The study results highlight the application potential of big data technology in the field of education and provides important a scientific basis and practical guidance for future education models.

Keywords: English wisdom classroom; Big data mining; Teaching application; Controlled experiment

Cite As

H. Gao, X. Wei, X. Tang, "Construction of English Wisdom Classroom Based on Educational Big
Data Mining", Engineering Intelligent Systems, vol. 34 no. 1, pp. 39-47, 2026.




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Published

2026-01-01

Issue

Vol. 34 No. 1 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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