Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation

Simple item page
dc.contributor.authorAraka, Eric
dc.contributor.authorOboko, Robert
dc.contributor.authorMaina, Elizaphan
dc.contributor.authorGitonga, Rhoda
dc.date.accessioned2023-07-17T12:59:38Z
dc.date.available2023-07-17T12:59:38Z
dc.date.issued2022
dc.descriptionArticleen_US
dc.description.abstractWith 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.en_US
dc.identifier.citationAraka, 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.en_US
dc.identifier.issn1492-3831
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/26265
dc.language.isoenen_US
dc.publisherInternational Review of Research in Open and Distributed Learningen_US
dc.subjecteducational data miningen_US
dc.subjectEDMen_US
dc.subjectself-regulated learningen_US
dc.subjectSRL profileen_US
dc.subjectalgorithmen_US
dc.subjectagglomerative hierarchical clusteringen_US
dc.subjectclustering algorithmen_US
dc.titleUsing Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluationen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning An Empirical Evaluation.pdf
Size:
1.91 MB
Format:
Adobe Portable Document Format
Description:
Full text Article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
RP-Department of Computing & Information Technology