Digital Footprints of Academic Success: An Empirical Analysis of Moodle Logs and Traditional Factors for Student Performance

With the wide adoption of Learning Management Systems (LMSs) in educational institutions, ample data have become available demonstrating students’ online behavior. Digital traces are widely applicable in Learning Analytics (LA). This study aims to explore and extract behavioral features from Moodle logs and examine their effect on undergraduate students’ performance. Additionally, traditional factors such as demographics, academic history, family background, and attendance data were examined, highlighting the prominent features that affect student performance. From January to April 2019, a total of 64,231 students’ Moodle logs were collected from a private university in Malaysia for analyzing students’ behavior. Exploratory Data Analysis, correlation, statistical tests, and post hoc analysis were conducted. This study reveals that age is found to be inversely correlated with student performance. Tutorial attendance and parents’ occupations play a crucial role in students’ performance. Additionally, it was found that online engagement during the weekend and nighttime positively correlates with academic performance, representing a 10% relative increase in the student’s exam score. Ultimately, it was found that course views, forum creation, overall assignment interaction, and time spent on the platform were among the top LMS variables that showed a statistically significant difference between successful and failed students. In the future, clustering analysis can be performed in order to reveal heterogeneous groups of students along with specific course-content-based logs.