Abstract
In recent years, the country is in an environment of rapid development, vigorously promoting scientific and technological innovation, and continuously increasing investment in university research, and the university itself also attaches great importance to scientific research. In the current background that university teachers pay much attention to teaching evaluation, universities are using Rough set data mining technology to better establish the teaching evaluation system so as to better evaluate their own teaching. In the context of smart education, education big data has been generally valued, and college student classroom behavior data as teaching process data has become a research hotspot in modern education. This paper takes the basic concepts and related technologies of Rough set theory and data mining as the theoretical basis of the research, and integrates its important content to analyze and research the improvement of the evaluation of college teachers’ wisdom teaching. This paper takes the data mining of the classic Rough set as the research object, and optimizes and improves the decision number method of teaching evaluation. Data mining through Rough sets can be regarded as an adaptive sorting algorithm. Therefore, the application of data mining technology in smart campuses of colleges and universities can create many opportunities and provide many conveniences for the development of colleges and universities by improving the evaluation of smart teaching of the teacher team. It is also conducive to comprehensively improving the management capabilities of universities. The experimental results show that this research has a better effect on using Rough set and data mining to evaluate the wisdom of teachers in colleges and universities.
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Acknowledgments
This work was supported by the 13th Five Year Plan Project of Education Science in Jilin Province in 2019 under Grant ZD19086.
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Su, D. (2021). Application of Data Mining Based on Rough Set in the Evaluation of University Teachers’ Wisdom Teaching. In: Abawajy, J., Xu, Z., Atiquzzaman, M., Zhang, X. (eds) 2021 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-79197-1_81
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DOI: https://doi.org/10.1007/978-3-030-79197-1_81
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