Student Log File Mapping Using K-Means Clustering

Abstract

The purpose of this article is to outline the structure of management One of the important characteristics of e-learning platforms is that students can take courses at any time, and they are not required to complete all available learning activities at one time. In Moodle, log data is valuable information that contains the activities of course users and course instructors. The data recorded in the moodle data log can be in the form of activity data, assignment timestamps, and ranking or grade grades. Log data exploration of educational data or educational data mining can be used to facilitate monitoring and see what activities are often carried out by course participants on the Moodle platform. One of the techniques used in data mining log data analysis is cluster analysis. Cluster analysis is the process of grouping data into groups whose members have similar characteristics. K-means clustering is one of the most frequently used cluster analysis algorithms. Based on the output, it can be seen that the members of cluster 1 are students with id 1,3,4,5, and 9. Then for cluster 2 are students with id 2,8,10,12 which in cluster 2 the highest average student click ,. and finally cluster 3 is filled with students with id 6, 7, and 11. It can be concluded that the second cluster is a collection of students who are active in accessing the LMS during learning.