Student graduation time prediction using intelligent K-Medoids Algorithm

Abstract

We proposed unsupervised learning, the Intelligent K-Medoids Algorithm to predict, the length of a study time of universitys students. This algorithm automatically clusters all students based on their 25 weighted scores from 25 different subject as the features. We tested the implementation of the algorithm using 240 students scores. These 240 students have graduated and their graduation time is considered for labeling the cluster. The result is 7 clusters with silhouette value of 0.2416. Each cluster is labeled according to the range of student graduation time. The range in each cluster exists due to the existence of students whose majority of scores are similar, but their graduation times are different. Academic leaving or extending the completion of thesis are the other factors contributing the range graduation time in each cluster. The prediction by k-folding 240 data to 5 subsets results average prediction accuracy of 99.58.