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University english teaching evaluation using artificial intelligence and data mining technology.

Qiuyang Huang1, Wenling Li2, Mohd Mokhtar Bin Muhamad3

  • 1School of Economics and Management, Jiangxi Arts & Ceramics Technology Institute, Jingdezhen, 333499, China.

Scientific Reports
|August 19, 2025
PubMed
Summary
This summary is machine-generated.

This study uses deep learning and AI to improve university English teaching evaluations. The Transformer architecture enhances personalized learning and accurately predicts exam success by analyzing student performance data.

Keywords:
Artificial intelligenceData miningDeep learningTeaching assessmentTransformer architectureUniversity english

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Natural Language Processing

Background:

  • Traditional English teaching evaluations often lack objectivity and personalization.
  • There is a need for innovative methods to assess English proficiency and guide personalized instruction.
  • Deep learning (DL) and artificial intelligence (AI) offer potential solutions for data-driven educational assessment.

Purpose of the Study:

  • To develop a reliable and efficient method for university English teaching evaluation using DL and AI.
  • To explore innovative teaching models and personalized strategies through a Bayesian framework.
  • To apply the Transformer architecture for enhanced understanding and evaluation of student English proficiency.

Main Methods:

  • Utilized deep learning and AI-driven data mining for English teaching evaluation.
  • Applied the Transformer architecture for feature extraction and sequence modeling of evaluation data.
  • Collected and analyzed data from Computer Science students at Tianjin University of Science and Technology.

Main Results:

  • Over 70% of students engage in active English learning sporadically, with females showing a higher tendency.
  • Listening and speaking skills are recognized as important by over 80% of males and 90% of females.
  • Scores in various question types significantly influence exam passing rates, reflecting knowledge and application abilities.

Conclusions:

  • The Transformer architecture, applied from natural language processing to education, enables interdisciplinary innovation.
  • The proposed AI-based method enhances objectivity and accuracy in teaching evaluations, reducing human bias.
  • This approach provides new solutions for educational challenges and supports personalized English instruction.