Izvorni znanstveni članak
https://doi.org/10.7906/indecs.23.5.4
Data and Process Mining in Analysing Student Behaviour
Snježana Križanić
orcid.org/0000-0002-0834-1710
; University of Zagreb, Faculty of organization and informatics, Varaždin, Croatia
*
Katarina Tomičić Pupek
orcid.org/0000-0001-6188-7395
; University of Zagreb, Faculty of organization and informatics, Varaždin, Croatia
Neven Vrček
; University of Zagreb, Faculty of organization and informatics, Varaždin, Croatia
* Dopisni autor.
Sažetak
The diversity of students’ learning paths is crucial for acquiring knowledge. Although there are digital learning environments that provide many opportunities for managing the learning process, the rapid development of technologies can cause disruptions in the realisation of targeted engagement scenarios. Monitoring educational content use and increasing interaction frequency can contribute to better performance management and achievement of learning outcomes.
Data and process mining methods and tools play a significant role in the research of performance and deviations. Anonymized real data from one elective university course was collected and processed to create a dataset for the application of clustering and decision tree analysis in the KNIME Analytics Platform and for creating a process model in a process mining tool. The results show behavioural patterns for three clusters and provide insight into interaction types by identifying variables related to content engagement as effective discriminators for student grouping. The process model illustrates the diversity of engagement in choosing learning paths through the course (based on 51 cases performing 52 distinct activities with an average of 233 activities), while retaining the focus on the assignment deliverables. Insights obtained from the analyses are useful for the effective implementation of digital learning environments illustrating that no exceptional scenarios occurred in the course in terms of deviations in behaviour with the digital learning platform in relation to similar teaching and learning paradigms provided by the same teachers and that more interactive features combined with new technologies would be useful in providing more personalized learning paths.
Ključne riječi
data mining; clustering; decision tree; process mining; educational data
Hrčak ID:
337373
URI
Datum izdavanja:
31.10.2025.
Posjeta: 131 *