Students' Academic Performance Analysis by K-Means Clustering for Investigating Students' Health Conditions within Clusters

Abstract

Schooling systems always offer finest teaching and learning opportunities to reach the educational requirements and ensuring achievement for every student. However, health affects students' academic performance directly. All teachers monitor their students' progress throughout the year, includes formative assessment, attendance rates, involvement in the organization, etc. This practice helps teachers continually assess the conditions of students and their academic performance. 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 further investigate the effect of sickness of students within a cluster that may lead to poor academic performance.

Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm

“…Partitioned clustering tries to divide a data set into disjoint subsets in order to find the best clustering criteria. Researchers have divided clustering analysis into four categories to address these specific practical issues: partition-based methods, hierarchical methods, density-based methods, and grid-based methods [ 15 ]. Clustering algorithms are an important step in the modernization of education, which promotes the cultivation of innovative talents and improves the quality of all people.…”