Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation
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Date
2022
Authors
Araka, Eric
Oboko, Robert
Maina, Elizaphan
Gitonga, Rhoda
Journal Title
Journal ISSN
Volume Title
Publisher
International Review of Research in Open and Distributed Learning
Abstract
With the increased emphasis on the benefits of self-regulated learning (SRL), it is important to make use of
the huge amounts of educational data generated from online learning environments to identify the
appropriate educational data mining (EDM) techniques that can help explore and understand online
learners’ behavioral patterns. Understanding learner behaviors helps us gain more insights into the right
types of interventions that can be offered to online learners who currently receive limited support from
instructors as compared to their counterparts in traditional face-to-face classrooms. In view of this, our
study first identified an optimal EDM algorithm by empirically evaluating the potential of three clustering
algorithms (expectation-maximization, agglomerative hierarchical, and k-means) to identify SRL profiles
using trace data collected from the Open University of the UK. Results revealed that agglomerative
hierarchical was the optimal algorithm, with four clusters. From the four clusters, four SRL profiles were
identified: poor self-regulators, intermediate self-regulators, good self-regulators, and exemplary selfregulators. Second, through correlation analysis, our study established that there is a significant
relationship between the SRL profiles and students’ final results. Based on our findings, we recommend
agglomerative hierarchical as the optimal algorithm to identify SRL profiles in online learning
environments. Furthermore, these profiles could provide insights on how to design a learning management
system which could promote SRL, based on learner behaviors.
Description
Article
Keywords
educational data mining, EDM, self-regulated learning, SRL profile, algorithm, agglomerative hierarchical clustering, clustering algorithm
Citation
Araka, E., Oboko, R., Maina, E., & Gitonga, R. (2022). Using educational data mining techniques to identify profiles in self-regulated learning: an empirical evaluation. The International Review of Research in Open and Distributed Learning, 23(1), 131-162.