Journal of Applied Science and Engineering

Published by Tamkang University Press

ESCI jase impact factor scopus logo open access rate of Scopus journal

Construction of a Teaching Quality Monitoring and Evaluation System Based on Data Mining Analysis

 2026

 Volume 35

Yichen Dong

School of Physical Education, Beijing Jiaotong University, Beijing 100124, China

Received: June 08, 2026
Accepted: August 13, 2026
Publication Date: September 25, 2026

上傳圖片

Minimum support is fixed to 40% of test results

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: BibTeX | http://dx.doi.org/10.6180/jase.202612_35.071

Download PDF

In our country, the teacher team construction and management level are constantly improving. It is crucial to enhance education quality, particularly classroom teaching quality. This study explores the application of data mining techniques in teaching quality evaluation. Using a dataset comprising evaluation scores from 10 teachers at a university, we construct a prototype system to demonstrate the proposed methodology, this paper studied the large data mining technology and clustering analysis algorithm, and based on this algorithm, put forward the teaching quality control and evaluation system, and choose a college teacher to students train the system, finally, the application of this system, the application effect is good. The specific conclusions are as follows: (1) compared with Apriori algorithm, FP-growth algorithm and HBE-Apriori algorithm, this algorithm has obvious advantages in operation speed. (2) The theoretical basis of K-mean clustering and the method of evaluating K-mean clustering are given, specific procedures and implementation procedures are given, and finally an example is given to illustrate its process and operation process. There are many ways of evaluation, with an emphasis on analyzing the values of purity and F1. (3) expounds three levels of the platform: performance level, service level and data level. Then, the specific implementation schemes of system initialization design, data collection, data preprocessing and data mining are expounded. (4) There is a close connection between teachers ‘education quality and students’ education quality. The corresponding “satisfaction” of “excellent” teachers is 0.8; the education quality of teachers is “good”; if the teaching content is good and the level evaluation confidence is 0.7, the confidence level is 0.5 regardless of whether the level evaluation is satisfactory or basically satisfied, significantly lower than both “good” and “level evaluation”. Another important element is the reasonable arrangement and optimization of the course content. This study employed evaluation scores from ten faculty members at a university as a preliminary pilot dataset to demonstrate the proposed methodological workflow, thereby constructing a prototype system. We explicitly acknowledge that this sample size constitutes the primary limitation of the study, and the generalizability of the conclusions requires further validation. When teaching content is rated as ‘excellent, the confidence level for a ‘satisfactory’ evaluation grade is 0.7; when teaching content is rated as ‘good,’ the confidence level for both ‘satisfactory’ and ‘basically satisfied’ evaluations is 0.5-significantly lower than the level for ‘excellent’ teaching content.

Keywords: Big Data, Association Rules, K-means Clustering, Teaching Quality Evaluation, Data Mining

  1. [1] J. A. Arribas Díaz and C. Martínez-Mediano, (2018) “The impact of ISO quality management systems on primary and secondary schools in Spain” Qual. Assur. Educ. 26: 2-24. DOI: https://doi.org/10.1108/QAE-06-2016-0028.
  2. [2] A. Gogile and B. Alemayehu, (2019) “The Status of Secondary School Science Laboratory Activities for Quality Education in Wolaita Zone, Southern Ethiopia” Journal of Education and Practice 7: 1-11.
  3. [3] T. Ploom Kand Haldma, (2013) “Balanced performance management in the public education system an empirical study of Estonian general education schools” Baltic Journal of Management 8: 183-207. DOI: https://doi.org/10.1108/17465261311310018.
  4. [4] K. Chung and Seolhyunsoo, (2017) “An analysis of relationship among evaluation domains, evaluation indicators, and evaluation results: focusing on a case of office of education” Journal of Holistic Convergence Education 21: 81-99.
  5. [5] K. Kayikci, (2013) “Unionization in the Public and Education Sector in Turkey, and Expectations of School Administrators and Teachers Expectations from Unions” Amme Idaresi Dergisi 46: 99-126.
  6. [6] T. R. W, (1986) “Changing concepts of educational evaluation” Journal of Education Research 10: 1-113. DOI: https://doi.org/10.1016/0883-0355(86)90008-X.
  7. [7] H. Hassani, C. Beneki, S. Unger, M. R. Mazinani, and M. R. Yeganegi, (2020) “Text Mining in Big Data Analytics” Big Data and Cognitive Computing 4(1): 1. DOI: https://doi.org/10.3390/bdcc4010001.
  8. [8] L. Y. Chen M Mao S, (2014) “Big Data: A Survey” Mobile Networks & Applications 19: 171-209. DOI: https://doi.org/10.1007/s11036-013-0489-0.
  9. [9] D. Laney, (2001) “3D data management: controlling data volume, Velocity, and Variety” Open Journal of Social Sciences 12:
  10. [10] J. Gantz and D. Reinsel, (2011) “Extracting value from chaos” IDC iView:
  11. [11] J. Kim and K. Chung, (2019) “Associative feature information extraction using text mining from health big data” Wireless Personal Communications 105: 691-707. DOI: https://doi.org/10.1007/s11277-018-5722-5.
  12. [12] X. F. Meng, (2013) “Big Data management: Concept, technology, and challenges” Computer Research and Development 50: 146.
  13. [13] X. Cui, P. Zhu, X. Yang, K. Li, and C. Ji, (2021) “Optimized big data K-means clustering using MapReduce” Applied Energy 304: 117766. DOI: https://doi.org/10.1016/j.apenergy.2021.117766.
  14. [14] X. Zhang, (2011) “Data Warehouse and Data Mining Technology” Beijing: Tsinghua University Press:
  15. [15] C. Zhou, (2011) “Research and implementation of the intrusion detection system based on data mining” Nanjing University of Information Science and Technology: DOI: https://doi.org/10.1109/IMCEC46724.2019.8983937.
  16. [16] J. Pan, (2012) “The fourth paradigm: Data-intensive scientific discovery” Beijing: Science Press:
  17. [17] P. Mika, (2014) “Flink: Semantic web technology for the extraction and analysis of social networks” Journal of Web Semantics 3: 224-241. DOI: https://doi.org/10.1016/j.apenergy.2021.117766.
  18. [18] S. Zhang, K. Lu, W. Liu, and X. Yin, (2015) “Generating associated knowledge flow in large-scale web pages based on user interaction” International Journal of Computer Systems Science & Engineering 5: 377-389.
  19. [19] V. Berikov, (2014) “Weighted ensemble of algorithms for complex data clustering” Pattern Recognition Letters 38: 99-106. DOI: https://doi.org/10.1016/j.patrec.2013.11.012.
  20. [20] W. Fang, Z. Ling, X. Wang, and S. Dong, (2014) “Toward a semantic search engine based on ontology” Proceedings of the Fourth International Conference on Machine Learning and Cybernetics: 1913-1918. DOI: https://doi.org/10.1109/ICMLC.2005.1527258.
  21. [21] T. Wang and S. Yin, (2026) “Enhancing Sustainability of Online Education via Validated Cloud Management Framework” IFS/ACM/Transactions/on/Machine/Learning 3: 9-19. DOI: https://doi.org/10.70891/TML.2026.040004.
  22. [22] L. AA, V. Estrela, H. Li, and S. Yin, (2024) “Quality of experience assessment in virtual/augmented reality serious games for healthcare: A systematic literature review” Technology and Disability 36: 17-28. DOI: https://doi.org/10.3233/TAD-230035.
  23. [23] A. Laghari, V. Estrela, and S. Yin, (2024) “How to collect and interpret medical pictures captured in highly challenging environments that range from nanoscale to hyperspectral imaging” Current Medical Imaging 20: e281222212228. DOI: https://doi.org/10.2174/0115734056258048231002055946.
  24. [24] A. Laghari, Y. Sun, M. Alhussein, K. Aurangzeb, M. S. Anwar, and M. Rashid, (2023) “Deep residual-dense network based on bidirectional recurrent neural network for atrial fibrillation detection” Scientific Reports 13: 15109. DOI: https://doi.org/10.1038/s41598-023-42080-7.
  25. [25] A. Laghari, S. Shahid, R. Yadav, S. Karim, A. Khan, H. Li, and S. Yin, (2023) “The state of art and review on video streaming” Journal of High Speed Networks 29: 211-236. DOI: https://doi.org/10.3233/JHS-222027.
  26. [26] S. Yin, H. Li, L. Teng, A. Laghari, A. Almadhor, M. Gregus, and G. Sampedro, (2024) “Brain CT image classification based on mask RCNN and attention mechanism” Scientific Reports 14: 29300. DOI: https://doi.org/10.1038/s41598-024-80281-w.
  27. [27] A. Laghari, A. Jumani, Y. S, M. Alvi, K. Kumar, H. Li, and A. Khan, (2026) “Blockchain Security and Privacy: Threats, Solutions, and Future Directions” Web Intelligence 24: 37-55. DOI: https://doi.org/10.3233/WEB-240023.
  28. [28] Y. Chen and L. Zhang, (2023) “Educational data mining: A review of recent advances and applications” Journal of Educational Computing Research 61: 823-856. DOI: https://doi.org/10.1177/07356331231156234.
  29. [29] H. Liu and F. Wang, (2024) “Big data analytics in higher education: A systematic review” Educational Technology & Society 27: 112-128. DOI: https://doi.org/10.30191/ETS.202401_27(1).0008.
  30. [30] J. Wu and X. Chen, (2023) “A hybrid approach for teaching quality evaluation using data mining techniques” IEEE Transactions on Learning Technologies 16: 412-425. DOI: https://doi.org/10.1109/TLT.2023.3245678.
  31. [31] Y. Zhao, S. Li, and Q. Zhang, (2024) “Research on intelligent evaluation of classroom teaching quality based on improved clustering algorithm” Modern Educational Technology 34: 45-53. DOI: https://doi.org/10.3969/j.issn.1009-8097.2024.02.006.
  32. [32] R. Xu and H. Yang, (2023) “Association rule mining in educational evaluation: A comprehensive survey” Journal of Educational Data Mining 15: 78-102. DOI: https://doi.org/10.5281/zenodo.7894567.
  33. [33] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu, (2024) “Online teaching quality evaluation system based on big data and cloud computing” International Journal of Emerging Technologies in Learning 19: 67-82. DOI: https://doi.org/10.3991/ijet.v19i01.45678.
  34. [34] J. Han and M. Kamber, (2022) “Data Mining: Concepts and Techniques (4th ed.)” DOI: https://doi.org/10.1016/C2020-0-03281-8.
  35. [35] P. Tan, M. Steinbach, and V. Kumar, (2023) “Introduction to Data Mining (2nd ed.)”
  36. [36] I. Witten, E. Frank, and M. A. Hall, (2023) “Data Mining: Practical Machine Learning Tools and Techniques (4th ed.)” Morgan Kaufmann: DOI: https://doi.org/10.1016/C2021-0-00472-9.
  37. [37] W. Zhang and L. Chen, (2024) “Educational evaluation in the era of artificial intelligence: opportunities and challenges” China Educational Technology 43: 78-86.
  38. [38] H. Wang and Y. Liu, (2023) “Application of clustering algorithms in teaching quality assessment” Computer Engineering & Science 45: 1492-1501. DOI: https://doi.org/10.3969/j.issn.1007-130X.2023.08.018.
  39. [39] Q. Sun and J. Li, (2024) “Research on university teaching quality monitoring system based on data mining” Journal of Higher Education 45: 56-64.
  40. [40] X. Zhu and H. Chen, (2023) “A survey of educational data mining techniques” Journal of Educational Technology Development and Exchange 16: 34-56. DOI: https://doi.org/10.18785/jetde.1601.03.