Knowledge-Based English Teaching Quality Assessment Methods: Using Data Mining Techniques to Identify Key Factors and Improve Teaching Strategies
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Abstract
Effective evaluation of complex learning systems requires reliable information acquisition, multidimensional data analysis, and adaptive feedback mechanisms. This study proposes a data-driven assessment framework for teaching-quality evaluation based on knowledge-oriented learning analytics and data-mining techniques. A multidimensional data acquisition architecture is developed to integrate classroom interaction records, online learning behaviors, assessment outcomes, and learner-affective information into a unified information-processing workflow. Multiple analytical modules, including cluster analysis, association-rule mining, sequence-pattern discovery, and predictive modeling, are employed to identify latent relationships between learning behaviors and educational outcomes. To improve evaluation reliability, a dynamic comprehensive indicator model is established for continuous quality monitoring and anomaly detection. Furthermore, an intelligent feedback mechanism is introduced to generate diagnostic reports, support adaptive strategy adjustment, and establish a closed-loop optimization process. The proposed framework enables systematic information extraction, pattern recognition, and decision support from heterogeneous educational datasets. By integrating data acquisition, intelligent analytics, and adaptive feedback, the framework provides an engineering-oriented methodology for information processing, behavior modeling, and dynamic performance assessment in complex data-intensive environments.
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References
Y. Gao, “Deep learning-based strategies for evaluating and enhancing university teaching quality,” Computers and Education: Artificial Intelligence, vol. 8, 2025, doi: 10.1016/j.caeai.2025.100362.
Z. Y. Zhang, Q. Gao, and F. Chen, “Evaluating English Language Teaching Quality in Classrooms Using OLAP and SVM Algorithms,” Mobile information systems, vol. 2022, no. Pt.4, pp. 9327669.1-9327669, 2022, doi: 10.1155/2022/9327669.
Y. Wang, Y. Zhang, and Z. G. Dong, “Neural Network-Based Approach for Evaluating College English Teaching Methodology,” Mathematical Problems in Engineering: Theory, Methods and Applications, vol. 2022, no. 9, pp. 1.1-1.8, 2022, doi: 10.1155/2022/2371583.
Q. L. Wang, “The Application of Big Data Fuzzy K-means Clustering in Evaluating English Teaching Ability,” Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence, pp. 318-322, 2023, doi: 10.1145/3660043.3660100.
S. S. Muttar and D. U. Al-Arnoosy, “Evaluating the Curriculum of Teaching Methods of Basic Education Colleges from the Points of View of the Teachers,” International Journal of Early Childhood Special Education, vol. 13, no. 2, 2021, doi: 10.9756/int-jecse/v13i2.211093.
L. Cheng, L. Yu, and X. Lan, “A study on the application of data envelopment analysis in evaluating the quality of physical education teaching in universities,” International Journal of Computational Systems Engineering, vol. 8, no. 1/2, pp. 107-119, 2024, doi: 10.1504/ijcsyse.2024.10058119.
P. Hofrichterová and D. Fait, “METHODS FOR EVALUATING TECHNICAL DRAWING AIMED AT IMPROVING THE QUALITY OF TEACHING AND INCREASING STUDENTS’ LEVELS OF KNOWLEDGE,” EDULEARN Proceedings, pp. 3584-3591, 2023, doi: 10.21125/edulearn.2023.0972.
Z. Asayesh, S. A. T. Lotfi, and S. A. H. Sarkeshikian, “Evaluating Speech Acts in English Language Teaching Software: The Case of Tell Me More,” Imam Khomeini International University, vol. 7, no. 2, pp. 29-50, 2020, doi: 10.30479/JMRELS.2020.11660.1451.
S. Nurhidayah, A. Faridi, and Z. Shakiyya, “Evaluating the Appropriateness of English Textbook Used for Teaching English for Specific Purposes,” English Education Journal, vol. 13, no. 1, pp. 140-151, 2023, doi: 10.15294/eej.v13i1.70649.
J. R. Chen, “A Method for Evaluating English Teaching Quality Based on Modal Data Analysis,” Learning and Analytics in Intelligent Systems, vol. 38, pp. 81-90, 2024, doi: 10.1007/978-3-031-60777-6_8.
C. Zhang, W. M. Cui, and X. Zhang, “A Method for Evaluating the Quality of Teaching Reform Based on Fuzzy Comprehensive Evaluation,” International Journal of Business Intelligence and Data Mining, vol. 25, no. 1, pp. 239-252, 2024, doi: 10.1504/IJBIDM.2024.140886.
R. A. Rass, “Arab Students’ Writing in English at the College Level,” Leiden, Netherlands: Brill; ro 2024. 169-204.
B. Pokharel, “Formative Assessment in English Language Teaching,” in Giri RA, Gnawali L, editor. The Handbook of English Language Education in Nepal. London: Routledge India, 2025.276P.
A. Sarasa-Cabezuelo and S. C. Llobet, “A Tool for Evaluating the Quality of Online Teaching,” Lecture Notes in Networks and Systems, vol. 349, pp. 383-395, 2022, doi: 10.1007/978-3-030-90677-1_37.
N. Li, “A Deep Learning Algorithm for Evaluating the Quality of English Teaching,” International Journal of Computational Intelligence and Applications, vol. 22, no. 03, Art. no. 2350011, 2023, doi: 10.1142/s1469026823500116.