Research on the Application of K-Means Clustering Algorithm in Student Achievement

This paper uses the K-Means algorithm in data mining to perform cluster analysis based on the final grade data of students majoring in software and information services in a certain university to effectively divide the sample data set. Through the analysis of the cluster analysis results, the charac...

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Vydáno v:	2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) s. 435 - 438
Hlavní autor:	Chi, Dianwei
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 15.01.2021
Témata:	clustering Clustering algorithms decision support Information services K-Means personalized tutoring Prediction algorithms project grouping project-driven Software Software algorithms student performance Training Visualization
On-line přístup:	Získat plný text
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Shrnutí:	This paper uses the K-Means algorithm in data mining to perform cluster analysis based on the final grade data of students majoring in software and information services in a certain university to effectively divide the sample data set. Through the analysis of the cluster analysis results, the characteristics of the distribution of student performance in each cluster category are refined, which provides a reference for teachers in project grouping and personalized teaching in the "project-driven" mode of teaching. At the same time, according to the visual analysis of the clustering effect in different clustering categories based on the performance of a single subject, the importance of courses can be predicted. Important courses can appropriately increase teachers or class hours. This provides scientific basis for better implementation of teaching reforms and revision of talent training programs.
DOI:	10.1109/ICCECE51280.2021.9342164