Li, Qiujie

Many college students struggle with regulating the time and effort they invest in classes. We used digital trace data from a learning management system to examine students' behavioral engagement and associations with course performance in four chemistry courses (N = 1596). Results from Study 1a show that behavioral engagement declined across the course, except for high spikes in exam weeks. Students with higher regularity and continued engagement after midterm exams obtained higher course grades, whereas steep increases in study activities shortly before exams did not predict performance. Using a selective subsample of students (n = 51, with 510 observations over time) who identified chemistry as a challenging course, Study 1b explores whether intentions to regulate learning behaviors with goal-directed control strategies lead to changes in behavioral engagement. Intentions to use control strategies lead to short-term changes in behavioral engagement, but students did not implement planned adjustments to their study behaviors in the long run.

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.

With the fast expansion of online learning in higher education, institutions have increasingly offered and mandated faculty professional development (PD) programs focused on online instruction. However, the extent to which these PD programs indeed lead to improved students' online course performance remains largely unknown. This paper used a rigorous quasi-experimental approach to estimate the impact of a PD program on student online course performance at a large community college using a dataset that includes more than 370,000 online course enrollments taught by close to 900 instructors. The analyses yielded robust, nonsignificant estimates for the PD program on both online course persistence and course grades. Further qualitative analysis of the courses taught by PD participants indicated that instructors' integration of elements covered by the PD training into their subsequent teaching was fairly limited, highlighting the need for ongoing support to help instructors incorporate recommended practices into instruction.

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.

This study fills a gap in knowledge regarding experienced instructors’ use of learning analytics, focusing on differences in their approach, the knowledge and skills they activate, and the development of these knowledge and skills. Through a qualitative analysis of think-aloud interviews with 13 analytics-experienced instructors, two distinct profiles of analytics use emerged. Instructors in the first profile prioritized monitoring student engagement and performance to foster desirable behaviors, using analytics to align students with course expectations. Instructors in the second profile focused on understanding student perceptions of learning, aligning the course design with diverse learning behaviors and needs. To arrive at such use, instructors went beyond mere acquisition of technical knowledge to also integrate pedagogical knowledge into their analytics practices. Lastly, the study uncovered specific learning analytics supports, such as ongoing individual consultations, invaluable for developing the needed technical and pedagogical knowledge. Together, the results of this study reveal the pivotal role of pedagogy in analytics use, calling for refinement of conceptual models and tailoring of practical support for instructors.