University english teaching evaluation using artificial intelligence and data mining technology
Abstract This work intends to drive reform and innovation in English teaching evaluation and support personalized English instruction. It utilizes deep learning (DL) and artificial intelligence (AI)-driven data mining technology to explore a reliable and efficient method for university English teach...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-16498-0 |
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| _version_ | 1849226313358901248 |
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| author | Qiuyang Huang Wenling Li Mohd Mokhtar bin Muhamad Nur Raihan binti Che Nawi Xutao Liu |
| author_facet | Qiuyang Huang Wenling Li Mohd Mokhtar bin Muhamad Nur Raihan binti Che Nawi Xutao Liu |
| author_sort | Qiuyang Huang |
| collection | DOAJ |
| description | Abstract This work intends to drive reform and innovation in English teaching evaluation and support personalized English instruction. It utilizes deep learning (DL) and artificial intelligence (AI)-driven data mining technology to explore a reliable and efficient method for university English teaching evaluation. By employing DL, this work explores innovative English teaching models and introduces a Bayesian framework to enable personalized teaching strategies. In the data mining process, the Transformer architecture is applied to English teaching evaluations. This capitalizes on its powerful feature extraction and sequence modeling capabilities to gain a comprehensive understanding and precise evaluation of students’ English proficiency. Additionally, an AI-based method for English teaching evaluation is proposed. Data from the English teaching and evaluation system for Computer Science students in the 2018 class at Tianjin University of Science and Technology are collected, analyzed, and processed. Group profiles of students are created to predict exam outcomes. The findings show that over 70% of students engage in active English learning only occasionally, with a higher proportion among females. More than 80% of males recognize the importance of listening and speaking skills, a sentiment shared by over 90% of female students. In terms of factors influencing students’ passing exams, scores in various question types play a central role, significantly impacting final grades. These scores reflect students’ mastery of English knowledge and application abilities. This work applies the Transformer architecture from natural language processing to the education domain, achieving interdisciplinary integration and innovation. This cross-disciplinary approach not only enriches teaching assessment methods but also provides new solutions for broader educational challenges. The proposed method enhances the objectivity and accuracy of teaching evaluation, minimizing the influence of human bias assessment results. |
| format | Article |
| id | doaj-art-e0a5d3ee340d4ede9b41c60d91e46a69 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e0a5d3ee340d4ede9b41c60d91e46a692025-08-24T11:28:11ZengNature PortfolioScientific Reports2045-23222025-08-0115111810.1038/s41598-025-16498-0University english teaching evaluation using artificial intelligence and data mining technologyQiuyang Huang0Wenling Li1Mohd Mokhtar bin Muhamad2Nur Raihan binti Che Nawi3Xutao Liu4School of Economics and Management, Jiangxi Arts & Ceramics Technology InstituteSchool of Education Science, Guangxi Minzu Normal UniversityFaculty of Educational Studies, Universiti Putra MalaysiaFaculty of Educational Studies, Universiti Putra MalaysiaSchool of Physical Education, Jiangsu University of Science and TechnologyAbstract This work intends to drive reform and innovation in English teaching evaluation and support personalized English instruction. It utilizes deep learning (DL) and artificial intelligence (AI)-driven data mining technology to explore a reliable and efficient method for university English teaching evaluation. By employing DL, this work explores innovative English teaching models and introduces a Bayesian framework to enable personalized teaching strategies. In the data mining process, the Transformer architecture is applied to English teaching evaluations. This capitalizes on its powerful feature extraction and sequence modeling capabilities to gain a comprehensive understanding and precise evaluation of students’ English proficiency. Additionally, an AI-based method for English teaching evaluation is proposed. Data from the English teaching and evaluation system for Computer Science students in the 2018 class at Tianjin University of Science and Technology are collected, analyzed, and processed. Group profiles of students are created to predict exam outcomes. The findings show that over 70% of students engage in active English learning only occasionally, with a higher proportion among females. More than 80% of males recognize the importance of listening and speaking skills, a sentiment shared by over 90% of female students. In terms of factors influencing students’ passing exams, scores in various question types play a central role, significantly impacting final grades. These scores reflect students’ mastery of English knowledge and application abilities. This work applies the Transformer architecture from natural language processing to the education domain, achieving interdisciplinary integration and innovation. This cross-disciplinary approach not only enriches teaching assessment methods but also provides new solutions for broader educational challenges. The proposed method enhances the objectivity and accuracy of teaching evaluation, minimizing the influence of human bias assessment results.https://doi.org/10.1038/s41598-025-16498-0Deep learningArtificial intelligenceData miningUniversity englishTeaching assessmentTransformer architecture |
| spellingShingle | Qiuyang Huang Wenling Li Mohd Mokhtar bin Muhamad Nur Raihan binti Che Nawi Xutao Liu University english teaching evaluation using artificial intelligence and data mining technology Scientific Reports Deep learning Artificial intelligence Data mining University english Teaching assessment Transformer architecture |
| title | University english teaching evaluation using artificial intelligence and data mining technology |
| title_full | University english teaching evaluation using artificial intelligence and data mining technology |
| title_fullStr | University english teaching evaluation using artificial intelligence and data mining technology |
| title_full_unstemmed | University english teaching evaluation using artificial intelligence and data mining technology |
| title_short | University english teaching evaluation using artificial intelligence and data mining technology |
| title_sort | university english teaching evaluation using artificial intelligence and data mining technology |
| topic | Deep learning Artificial intelligence Data mining University english Teaching assessment Transformer architecture |
| url | https://doi.org/10.1038/s41598-025-16498-0 |
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