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Students’ learning behaviour in programming education analysis: insights from entropy and community detection

Mai, Tai Tan orcid logoORCID: 0000-0001-6657-0872, Crane, Martin orcid logoORCID: 0000-0001-7598-3126 and Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113 (2023) Students’ learning behaviour in programming education analysis: insights from entropy and community detection. Entropy, 25 (8). pp. 1225-1247. ISSN 1099-4300

Abstract
The high dropout rates in programming courses emphasise the need for monitoring and understanding student engagement, enabling early interventions. This activity can be supported by insights into students’ learning behaviours and their relationship with academic performance, derived from student learning log data in learning management systems. However, the high dimensionality of such data, along with their numerous features, pose challenges to their analysis and interpretability. In this study, we introduce entropy-based metrics as a novel manner to represent students’ learning behaviours. Employing these metrics, in conjunction with a proven community detection method, we undertake an analysis of learning behaviours across higher- and lower-performing student communities. Furthermore, we examine the impact of the COVID-19 pandemic on these behaviours. The study is grounded in the analysis of empirical data from 391 Software Engineering students over three academic years. Our findings reveal that students in higher-performing communities typically tend to have lower volatility in entropy values and reach stable learning states earlier than their lower-performing counterparts. Importantly, this study provides evidence of the use of entropy as a simple yet insightful metric for educators to monitor study progress, enhance understanding of student engagement, and enable timely interventions.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:entropy; learning behaviours; learning analytics; educational data mining; community detection; random matrix theory
Subjects:Computer Science > Artificial intelligence
Computer Science > Information retrieval
Computer Science > Information technology
Computer Science > Machine learning
Social Sciences > Educational technology
Mathematics > Mathematical models
Mathematics > Mathematical physics
DCU Faculties and Centres:Research Initiatives and Centres > ADAPT
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Official URL:https://doi.org/10.3390/e25081225
Copyright Information:© 2023 The Authors.
Funders:Science Foundation Ireland under Grant Agreement No. 13/RC/2106 P2 at the ADAPT SFI Research Centre at DCU
ID Code:29302
Deposited On:21 Dec 2023 14:52 by Tai Mai . Last Modified 21 Dec 2023 14:59
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