Behavioral tracking analysis on learning management system with apriori association rules algorithm

Abstract

Online learning has been applied in various educational institutions, and have some positive effects on the conventional learning, especially if both learning is collaborated. Online learning helps teaching difficulties in conventional learning where learning process of individual student is hard to know in detailed by the teacher because of too many students in a single class. With online learning assisted by Learning Management System (LMS) teachers can know each student individual learning process by analyzing student log activity on the LMS which is often called by behavioral tracking. LMS used as research material is Moodle that has been applied to the Computer Science Education Department, UPI. The purpose of this research is to find learning status of each student by analyzing student's behavior while using the Moodle LMS. One of behavioral tracking model which can be used to determine the student's learning status is Monitoring Online Course with Log Data (MOCLog) model. By combining the concept map, and solution map, this model can analyze log data on Moodle LMS, and generate learning status of each individual student. Then the teacher can determine what behavioral traits are dominant, and most influential on learning with association rule data mining technique with apriori algorithms. This study provides that dominant learning status on the Computer Network course with 84.75 % students is Normal Learning Status while Frequent Access the Course to be an activity that greatly affect the student's learning status in the Moodle LMS course with 48 students.