Clustering of ESTS Test Scores of Students in the Faculty of Science and Technology at Loei Rajabhat University
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Abstract
This study aims to develop a web application for reporting English for Science and Technology (ESTS) examination results of students in the Faculty of Science and Technology, Loei Rajabhat University. The focus is on applying information technology to analyze test results and group student data using unsupervised learning via clustering, in order to effectively portray students’ overall English proficiency. The system was developed in PHP with a MySQL database and managed through phpMyAdmin. K-means clustering was employed, experimenting with the number of clusters from 2 to 20, using random initialization and Euclidean distance. Clustering quality was evaluated with the Davies–Bouldin Index (DBI). The experiments show that K = 3 yielded the lowest DBI of 0.82, indicating clear cluster separation. At the same time, increasing the number of clusters reduced the average within-cluster distance and lowered the DBI, suggesting a trend toward more compact and distinct groupings. With this system, users can conveniently access analytically grouped score reports, enabling a comprehensive assessment of students’ proficiency by cluster and supporting the planning of tailored English skill development aligned with learners’ ability levels
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