Using Learning Analytics to Predict Students Performance in Moodle LMS
Date
2020Item Type
ArticleAbstract
Today, it is almost impossible to implement teaching processes without using information and communication technologies (ICT), especially in higher education. Education institutions often use learning management systems (LMS), such as Moodle, Edmodo, Canvas, Schoology, Blackboard Learn, and others. When accessing these systems with their personal account, each student’s activity is recorded in a log file. Moodle system allows not only information saving. The plugins of this LMS provide a fast and accurate analysis of training statistics. Within the study, the capabilities of several Moodle plugins providing the assessment of students’ activity and success are reviewed. The research is aimed at discovering possibilities to improve the learning process and reduce the number of underperforming students. The activity logs of 124 participants are analyzed to identify the relations between the number of logs during the e-course and the final grades. In the study, a correlation analysis is performed to determine the impact of students’ educational activity in the Moodle system on the final assessment. The results reveal that gender affiliation correlates with the overall performance but does not affect the selection of training materials. Furthermore, it is shown that students who got the highest grades performed at least 210 logs during the course. It is noted that the prevailing part of students prefers to complete the tasks before the deadline. The study concludes that LMSs can be used to predict students’ success and stimulate better results during the study. The findings are proposed to be used in higher education institutions for early detection of students experiencing difficulties in a course.
Author
Zhang, Yaqun
Ghandour, Ahmad
Shestak, Viktor