Student profiles of change in formative assessment behaviour: Replication and evaluation for grade prediction

Abstract

As students learn and practice new skills in university courses, their behaviour can change in response to competing demands and increasing content complexity. However, most metrics used to evaluate study behaviour focus on the number or sequence of activities rather than on the change of behaviour. To address this, we replicate and extend a complex dynamical systems approach to characterise recurrence in behavioural patterns and whether it changes.Using assessment logs from 1362 students in the first 5 weeks of a semester-long programming course, we examine whether changes in the patterns of formative assessment submissions can differentiate student sub-groups and predict their performance. We identify three student profiles of behavioural change. We find that higher entropy of recurrence in assessment submission patterns is associated with better performance, and that changes in this entropy signal upcoming changes in performance. We also show that higher entropy of recurrence is associated with greater timeliness of submissions. Finally, we evaluate the predictive value of early behavioural patterns and find that while student profiles of change do not outperform conventional predictive metrics, they offer complementary insights that can enable timely interpretations of student data and inform interventions. Overall, our findings extend the generalisability of behavioural metrics based on complex dynamical systems by demonstrating consistent patterns across courses, LMS types and data sources.