Li, Qiujie

In prior literature on using clickstream data to capture student behavior in virtual learning environments, procrastination is typically measured by the extent to which students delay their coursework. However, students may delay coursework under personal and environmental contexts and not all delays should be considered procrastination. Thus, this study aims to identify different types of delayers and examine how they differ in academic engagement and performance. We utilized learning management system (LMS) data from three online undergraduate courses. Specifically, using data from the first three weeks of the course, we classified delayers into three subgroups – high-achieving, low-achieving, and sporadic delayers – based on the timing of their coursework access and submission, the consistency of these behaviors, and their short-term course performance. Our findings reveal that the subgroups significantly differ in course engagement and long-term performance. Low-achieving delayers exhibited the lowest levels of engagement and performance. While sporadic delayers and high-achieving delayers demonstrated comparable levels of engagement, the latter received higher course grades. These findings challenge commonly used LMS measures for procrastination, highlight the complexity of academic delays, and reveal nuanced patterns of student behavior. The results contribute to discussions on future interventions and research related to distinct forms of delays.

The enthusiasm for student-facing analytics as tools for supporting student self-regulation is overshadowed by uncertainties about their actual impact on student outcomes. This study aims to fill the gap in experimental evidence concerning student-facing analytics by implementing a randomized control trial. Specifically, we investigated the effects of data visualizations that display student level of content mastery in comparison to their peers, alongside recommendations for learning strategies. The preliminary results reveal that the intervention impacts student attribution and motivation in varying ways, based on their self-evaluation of their current course performance. Further analysis, including coding students' interpretation of the data visualization, will be conducted to uncover the diverse ways students might interpret the analytics.