Multilevel Clustering Comparison using Self-Organizing Map and K-Means for MIR Score Clustering

The theory of Multiple Intelligences has been widely applied in exchange of intelligence test approach with the single score (IQ). One of the applications of MI-based learning strategies is to group students based on Multiple Intelligence Research (MIR) scores. In this study, students are grouped based on MIR scores using multilevel clustering techniques. Multiple clustering is applied to meet the needs of the equal number of students and gender. Several models of multilevel clustering using Self-Organizing Map (SOM) and K-Means algorithms are carried out. The evaluation results show that the smallest error is generated by the multilevel SOM. This method can facilitate students grouping based on MIR scores by maintaining the similarity of student features and class heterogeneity. This clustering method is expected to be an efficient way to group students automatically and effectively according to MI-based learning strategies.