Application of K-means clustering algorithm for academic performance analysis in students’ college Japanese course

Sixuan Wang; Bin Luo

doi:10.1117/12.2668253

20 April 2023 Application of K-means clustering algorithm for academic performance analysis in students’ college Japanese course

Sixuan Wang, Bin Luo

Author Affiliations +

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 1260234 (2023) https://doi.org/10.1117/12.2668253
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Over the past few years, many universities are starting to set up the college Japanese course for undergraduate students. But many new situations and problems arise. So far, there has been relatively little research on these issues, not to mention research using an empirical approach. To address these issues and gain practical insights, this paper first collects the learning dataset of all first-year students taking a college Japanese course at a university. The program has a stable learning population, and a good research foundation that lasts for one year. Second, a clustering algorithm called KMeans was used to evaluate the student learning analysis. Specifically, we use the elbow technique to identify the Kvalues that work best. The empirical analysis showed that clusters performed best when the number of clusters was 2 which means students should be divided into two groups：the students evaluated as Good (49.43%), and the students evaluated as Fair (50.57%). In addition, we analyzed the learning performance of both groups and made practical suggestions for teaching. The findings of our study can provide a practical reference for higher education institutions and teaching staff.

Citation Download Citation

Sixuan Wang and Bin Luo "Application of K-means clustering algorithm for academic performance analysis in students’ college Japanese course", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 1260234 (20 April 2023); https://doi.org/10.1117/12.2668253

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KEYWORDS

Analytical research

Data mining

Distance measurement

Data processing

Databases

Design and modelling

Machine learning

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