Finding similar clustering pattern between students academic performance and non-curricular activities data

Students joined a number of non-curricular activities in order to develop critical thinking skills and organizational skills. Without a good time management, this kind of activity may bring negative effect, which is students tend to be more active in the organizations activities than trying to improve their academic performance. This study focuses on cluster analysis on variables related to non-curricular organizational activities and student academic performance. The K-Means algorithm has been used to divide student academic performance into two clusters, higher and lower GPA. The results are then compared to the clustering of students — also with K-Means — based on the noncurricular activity attributes of each student. Experiments that have been conducted show that more than 50 percent of samples show an equivalent clustering pattern between each cluster of academic performance and their correlated non-curricular activity cluster.