Research On College English Teaching And Quality Evaluation Based On Data Mining Technology

Lu Qian

doi:10.6180/jase.202304_26(4).0011

Research On College English Teaching And Quality Evaluation Based On Data Mining Technology

Computer Science and Information Engineering

Lu Qian This email address is being protected from spambots. You need JavaScript enabled to view it.^1,2

¹Jiangxi Industry and Trade Technical College, Jiangxi Province, 330038, China
²Jiangxi Police Institute, Nanchang City, Jiangxi Province, 330103, China

Received: May 19, 2022
Accepted: June 30, 2022
Publication Date: July 29, 2022

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: ||https://doi.org/10.6180/jase.202304_26(4).0011

ABSTRACT

The classroom teaching and quality evaluation of college English teachers is an effective way to evaluate the teaching quality and improve the teaching level of teachers. But there is uncertainty and fuzziness in the process of teaching and quality evaluation. Therefore, based on the data mining technology, the analytic hierarchy process is used to construct the evaluation index body. Then the indexes which have important influence on the evaluation result are selected as BP neural network input. Finally, the neural network teaching and quality evaluation model is established, and the improved algorithm is simulated. The validity of this method is verified by experiments, which can improve the accuracy of college English teaching and quality evaluation. With the help of SPSS andWeka data mining tools, this paper uses a variety of data mining means to conduct in-depth mining analysis of various teaching operation status data, explore the complex relationship behind the data and its value significance, then further propose how to effectively use data mining to implement teaching quality monitoring methods and measures, and discuss how to combine the analysis results for optimizing the use of student assessment data, innovating the way of college students The study also discusses how to combine the analysis results to optimize the use of student evaluation data and to innovate the way of guiding the quality assessment of English courses for college students, to provide meaningful references for the implementation of English teaching quality assessment.

Keywords: College English; Quality Evaluation; Level Analysis; BP Neural Network

REFERENCES

[1] D.-P. NI, (2017) “Study on Cooperative Learning in College English Teaching":
[2] T. Kjærgaard, (2017) “Rhizomes and plateaus: a study of digital communities of practice in University college English teaching" Designs for Learning:
[3] F. Zhang, (2017) “Quality-Improving Strategies of College English Teaching Based on Microlesson and Flipped Classroom." English Language Teaching 10(5): 243–249.
[4] X. Qin, (2017) “The Flipped Classroom and Its Application in College English Teaching" Education Teaching Forum 10(5): 74–76.
[5] M.-h. Sun, Y.-g. Li, and B. He, (2017) “Study on a quality evaluation method for college English classroom teaching" Future Internet 9(3): DOI: 10.3390/fi9030041.
[6] W. Zhao, (2016) “The construction of multiple evaluation system on class teaching quality of college English teachers" Journal of Anshan Normal University 10: 12–16.
[7] L.-p. ZHANG and X.-x. LIU, (2016) “Analyzing the Factors Influencing Students’ Evaluation of Teaching Quality in College English Classes through Partial Correlation Analysis" DEStech Transactions on Social Science, Education and Human Science: 35. DOI:10.12783/dtssehs/icssd2016/4699.
[8] x. Wei, (2017) “The implementation of the new evaluation system in college English teaching" Heilongjiang Science 7: 17–18.
[9] Z. Y. Shen, (2016) “Analysis and Reflection on Assessment of College English Classroom Teaching——Taking Taiyuan Institute of Technology as a Sample" Journal of Xian University 6: 66–67.
[10] X. Zhou and X. Zhu, (2016) “Standards-driven College English curriculum implementation and evaluation" Foreign Language Learning Theory and Practice 3: 32–37.
[11] J. Yang, Y. Li, Q. Liu, L. Li, A. Feng, T.Wang, S. Zheng, A. Xu, and J. Lyu, (2020) “Brief introduction of medical database and data mining technology in big data era" Journal of Evidence-Based Medicine 13(1): 57–69. DOI: 10.1111/jebm.12373.
[12] Z. S. Ageed, S. R. Zeebaree, M. M. Sadeeq, S. F. Kak, H. S. Yahia, M. R. Mahmood, and I. M. Ibrahim, (2021) “Comprehensive survey of big data mining approaches in cloud systems" Qubahan Academic Journal 1(2): 29–38.
[13] L. Li, (2020) “Analysis and data mining of intellectual property using GRNN and SVM" Personal and Ubiquitous Computing 24(1): 139–150. DOI: 10.1007/s00779-019-01344-8.