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. Practitioner notes What is already known about the topic Recurrence quantification analysis can capture dynamics of student behaviour. Prior work proposed a methodology based on recurrence of behaviour in a complex system to quantify study behaviour with trace data. Students whose behavioural patterns showed consistently high entropy of recurrence performed better. What this paper adds This study replicates a CDS‐based methodology in a new context, with a typical data source: traces of student assessment submissions. Dynamics‐based features are associated with the timeliness of student submissions and course performance. Dynamics‐based features do not outperform conventional LA features in predicting student performance, but offer complementary insights. Implications for policy/practice More research is needed to interpret what dynamics‐based features mean for teaching practice before they can be acted on. Future research and teaching activities could integrate interviews and self‐reported instruments to examine potential interpretations of dynamics‐based features, such as students' propensity to adapt.
Poquet et al. (Mon,) studied this question.