A multivariate approach to predicting student outcomes in web-enabled blended learning courses

Abstract This study aimed to develop a practical model for predicting students at risk of performing poorly in blended learning courses. Previous research suggests that analyzing usage data stored in the log files of modern Learning Management Systems (LMSs) would allow teachers to develop timely, evidence-based interventions to support at risk or struggling students. The analysis of students' tracking data from a Moodle LMS-supported blended learning course was the focus of this research in an effort to identify significant correlations between different online activities and course grade. Out of 29 LMS usage variables, 14 were found to be significant and were input in a stepwise multivariate regression which revealed that only four variables – Reading and posting messages , Content creation contribution , Quiz efforts and Number of files viewed – predicted 52% of the variance in the final student grade.

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