Examining the Effect of Academic Procrastination on Achievement Using LMS Data in e-Learning

This study aimed to investigate the effect of academic procrastination on e-learning course achievement. Because all of the interactions among students, instructors, and contents in an e-learning environment were automatically recorded in a learning management system (LMS), procrastination such as the delays in weekly scheduled learning and late submission of assignments could be identified from log data. Among 569 college students who enrolled in an e-learning course in Korea, the absence and late submission of assignments were chosen to measure academic procrastination in e-learning. Multiple regression analysis was conducted to examine the relationship between academic procrastination and course achievement. The results showed that the absence and late submission of assignments were negatively significant in predicting course achievement. Furthermore, the study explored the predictability of academic procrastination on course achievement at four points of the 15-week course to test its potential for early prediction. The results showed that the regression model at each time point significantly predicted course achievement, and the predictability increased as time passed. Based on the findings, practical implications for facilitating a successful e-learning environment were suggested, and the potential of analyzing LMS data was discussed.