Using Learning Analytics to Predict Students Performance in Moodle LMS

Yaqun Zhang, Ahmad Ghandour, Viktor Shestak

Abstract


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 sav-ing. The plugins of this LMS provide a fast and accurate analysis of training sta-tistics. 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 num-ber of underperforming students. The activity logs of 124 participants are ana-lyzed 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 assess-ment. The results reveal that gender affiliation correlates with the overall perfor-mance 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 dur-ing 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 pre-dict students' success and stimulate better results during the study. The findings are proposed to be used in higher education institutions for early detection of stu-dents experiencing difficulties in a course.

Keywords


learning management systems; Moodle; electronic journal file; Moodle plugin; student success; student behavior.

Full Text:

PDF


Copyright (c) 2020 Yaqun Zhang, Ahmad Ghandour, Viktor Shestak


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
Creative Commons License
Indexing:
Scopus logo Clarivate Analyatics ESCI logo EI Compendex logo IET Inspec logo DOAJ logo DBLP logo Learntechlib logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo