Flexibility or Procrastination? A Longitudinal Study of Lecture Engagement Patterns and Academic Performance

Chow, W., Li, Q., Guo, M., Peng, S., Cheong, M. & Marx, J. (2025). Flexibility or Procrastination? A Longitudinal Study of Lecture Engagement Patterns and Academic Performance. 36th Australasian Association for Engineering Education Annual Conference, Australasian Association for Engineering Education.

Access Status

Open Access

URI

https://hdl.handle.net/11343/366709

Abstract

CONTEXT The widespread adoption of lecture capture systems has increased learning flexibility but is also associated with a marked decline in on-campus attendance. While these systems give students greater control over when and how they learn, their effect on academic performance remains contested. Flexibility can act as a “double-edged sword”: it benefits self-regulated learners but may exacerbate procrastination among those with weaker time-management skills. This study explores how students navigate this flexibility and how their choices influence academic outcomes. PURPOSE OR GOAL This study moves beyond a binary comparison of in-person and online lecture use to examine how students engage with flexible learning modes over time. It aims to identify distinct engagement patterns among postgraduate students and analyse their relationship with procrastination and academic performance. Specifically, the research investigates which engagement behaviours support academic success and which contribute to detrimental outcomes such as procrastination. APPROACH OR METHODOLOGY/METHODS The study employed a mixed-methods sequential explanatory design over a 12-week semester with approximately 190 postgraduate students. In the quantitative phase, learning analytics data from the university’s system was used to track weekly student engagement with on-campus lectures, live streams, and recordings. Time-series K-means clustering was applied to this data to identify distinct behavioural patterns, which were then compared against academic performance using ANOVA. In the qualitative phase, 15 semi-structured interviews were conducted to provide deeper insight into the students’ experiences and the reasons for their engagement patterns. ACTUAL OR ANTICIPATED OUTCOMES The analysis revealed four distinct behavioural clusters: On-campus learners, Flexible learners, Online learners, and Disengaged students. The on-campus and flexible learner groups achieved the highest academic performance. Conversely, the disengaged students, who formed the largest cluster, had significantly lower marks across all assessments, demonstrating a clear link between their lack of engagement and poor performance. Qualitative findings indicated that students’ ability to self-regulate and the presence of peer accountability were critical factors in preventing procrastination. CONCLUSIONS/RECOMMENDATIONS/SUMMARY The findings confirm that unstructured flexibility is not universally beneficial; while it empowers highly self-regulated students, it can amplify procrastination and hinder the performance of a substantial portion of the student body. Rather than mandating attendance, institutions should implement supportive scaffolding to help students manage their autonomy. A practical recommendation is to introduce soft deadlines for viewing lecture recordings to provide necessary structure and urgency, mitigating procrastination without removing the benefits of flexible access. KEYWORDS: Lecture capture systems, student learning patterns, procrastination, performance

Publisher URL

https://aaee2025.org/program/

Collections

Computing and Information Systems - Research Publications
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