LAUSR

Abstract Procrastination has been increasing since the proliferation of online learning. While traditionally assessed with self-report instruments, online learning offers the possibility to measure objective indicators (log data). In the present study, we aim to find out whether the combination of short scales on procrastination-related traits and log data predict the extent of dilatory behaviour in online tasks and performance (assignment scores). The log data models (which include the number of clicks on the assignment, the interval between thematic block start and first click, and the number of clicks on course activities as predictors) have a better fit and explain more variance than the questionnaire models when predicting delay; and the predictions barely improve when combined. The prediction of performance did not yield any noteworthy effects. Future studies need to diversify predictors by incorporating contextual factors to improve early and/or late predictions and allow classification of dilatory behaviour (e.g. procrastination vs purposeful delay).