A Clustering Analysis of Students’ English Scores After Targeted Improvement

Authors

  • Xia Sun Department of Foreign Language, Hefei Normal University, Hefei, Anhui 230061, China

Abstract

In every school, the analysis of students’ scores is an important means of improving teaching outcomes. By analyzing these scores, pedagogical shortcomings can be identified and addressed so that students’ results in the future are better. However, the traditional way of assessing by using scores alone does not adequately capture all the information contained in students’ scores. In this study, the English scores obtained by 100 students selected for targeted improvement, were analyzed using the K-means clustering algorithm. The students were classified into four categories based on the elbow method. The first category had the highest average score for composition (22.00); the second category had the highest average score for listening (25.60) and reading (26.60); the third category had the highest average score for word selection (10.25) and reading (25.75); the average
scores of students in the fourth category were all unsatisfactory. The results of the study demonstrated that the K-means algorithm can analyze the characteristics of students’ English scores effectively, which can provide a sound basis for teachers to improve teaching methods and take appropriate measures to improve students’ English scores.

Keywords: clustering analysis, K-means clustering algorithm, score analysis

Cite As

X. Sun, "A Clustering Analysis of Students’ English Scores After Targeted Improvement", Engineering
Intelligent Systems, vol. 32 no. 2, pp. 89-93, 2024.





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Published

2024-03-01

Issue

Vol. 32 No. 2 (2024): International Journal of Engineering Intelligent Systems

Section

Article

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