Abstract
Exam is the most effective method to evaluate the quality of higher education, and improving higher education exam quality is of paramount importance. Traditional methods of analyzing and improving higher education exam quality, such as mean and variance based on mathematical statistics, are only suitable for sample datasets that are small and static. On the other hand, robust clustering methods, such as PAM and k-Medoids, do not consider the importance of each attribute which lead to different impacts on clustering result. Based on the aforementioned issues, this paper researches on improving higher education exam quality based on weighted k-Medoids clustering. Specifically, the calculating method of attribute weights is introduced based on the granularity rough entropy. Secondly, a novel weighted k-Medoids clustering method is proposed, which integrates the attribute weights into the classic k-Medoids clustering method. Finally, the performance on UCI datasets shows the proposed method significantly improves the clustering accuracy compared to PAM and fast k-Medoids. Meanwhile, the experimental results on proprietary artificial teaching datasets indicate that the novel method identifies and corrects redundant and less significant exam questions, effectively improving higher education exam quality.
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Acknowledgments
This work was partially supported by the National Key Research and Development Program of China [grant numbers 2022YFA1602200 and 2021YFA1000600], the National Natural Science Foundation of China [grant number 62072170 and 62202156], the Science and Technology Project of the Department of Communications of Hunan Provincial [grant number 202101], the Key Research and Development Program of Hunan Province [grant number 2022GK2015], the Hunan Provincial Teaching Research and Reform Project [grant number HNJG-2022-0786 and HNJG-2022-0792], the Hunan Provincial Department of Education Scientific Research Project[grant number 21C0946], the Hunan Provincial Degree and Graduate Teaching Reform and Research Project[grant number 2022JGYB130], the Teaching Reform and Research Project of Hunan University of Science and Technology [grant number 2021-76-9 and 2021-76-26].
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Chen, L. et al. (2024). Research on Improving Higher Education Exam Quality Based on Weighted k-Medoids Clustering. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Educational Digitalization. ICCSE 2023. Communications in Computer and Information Science, vol 2025. Springer, Singapore. https://doi.org/10.1007/978-981-97-0737-9_19
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DOI: https://doi.org/10.1007/978-981-97-0737-9_19
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