Prediction of Students' Academic Performance by K-Means Clustering

Abstract

Schooling system should offer finest teaching and learning opportunities to reach the
educational requirements and ensuring achievement for every student. All teachers monitor their
students’ progress throughout the year, includes formative assessment, questioning, providing
feedback, etc. This practice helps teachers continually assess students’ academic performance
and evaluate the effectiveness of their teaching. Data mining is a process to explore certain style
and hidden correlation among massive volume of data. Data mining is applied in multiple
disciplinary fields, for example, insurance, education, banking and bioinformatics. Data mining
skills such as clustering, classification, regression and prediction are commonly used by
educators to measure academic performance. In this paper, method of k-means clustering with
deterministic model is applied to analyze the student's overall performance. The students’
assessment scores are assigned to k clusters without prior knowledge of the scores. The result is
important for educators to identify students who are at risk academically and areas where
teaching strategies may need adjustment to better meet these students' needs.