Clustering Approach for Analyzing the Student’s Efficiency and Performance Based on Data

The academic community is currently confronting some challenges in terms of analyzing and evaluating the progress of a student’s academic performance. In the real world, classifying the performance of the students is a scientifically challenging task. Recently, some studies apply cluster analysis for evaluating the students’ results and utilize statistical techniques to part their score in regard to student’s performance. This approach, however, is not efficient. In this study, we combine two techniques, namely, k-mean and elbow clustering algorithm to evaluate the student’s performance. Based on this combination, the results of performance will be more accurate in analyzing and evaluating the progress of the student’s performance. In this study, the methodology has been implemented to define the diverse fascinating model taking the student test scores.