International Science and Technology Journal

The purpose of educational data mining is to extract important details from the field's existing data in order to find hidden, significant, and valuable information that can be used to enhance student performance. The research work in this paper uses cluster analysis to visualize the students' performance by grouping them according to certain qualities, comparing the results, and creating representations of the performance. This paper presents the k-means clustering algorithm as a simple and efficient tool to monitor the progression of students’ performance in higher institutions. The findings will be useful in identifying students who may struggle academically and in determining areas that require modifications to instructional practices in order to provide these students with better support. Keywords: Data analysis, educational data mining, k-means algorithm, clusters, and performance