Student profiles of change in formative assessment behaviour
Replication and evaluation for grade prediction
Bibliographic Data
| ID | 21297335 |
|---|---|
| Authors | Oleksandra Poquet (0000-0001-9782-816X, Professorship of Learning Analytics, School of Social Sciences and Technology Technical University of Munich Munich Germany), Jelena Jovanović (0000-0001-6134-8180, University of Belgrade Faculty of Organizational Sciences Belgrade Serbia), Stephan Krusche (0000-0002-4552-644X, Professorship of Applied Education Technologies, School of Computation, Information and Technology Technical University of Munich Munich Germany) |
| Year | 2026 |
| Publication date | 2026-05-04 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | British Journal of Educational Technology (JOURNAL) |
| Journal identifiers | ISSN: 0007-1013 • E-ISSN: 1467-8535 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/bjet.70070 |
| OpenAlex | W7160216055 |
| Language | EN |
| References cited | 31 |
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
Dynamic assessment · Entropy (arrow of time) · Formative assessment · Online assessment · Replicate · Replication (statistics) · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Teaching and Learning Programming
| Highly cited | No |
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