Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning
An Empirical Evaluation
Bibliographic Data
| ID | 21729549 |
|---|---|
| Authors | Eric Araka (0000-0001-9242-7471, Kenyatta University), Robert Oboko (University of Nairobi), Elizaphan Maina (0000-0003-1917-9760, Kenyatta University), Rhoda Gitonga (0000-0002-9625-8086, Kenyatta University) |
| Year | 2022 |
| Volume | 23 |
| Issue | 1 |
| Pages | 131-162 |
| Publication date | 2022-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The International Review of Research in Open and Distributed Learning (JOURNAL) |
| Journal identifiers | ISSN: 1492-3831 • E-ISSN: 1492-3831 |
| Publisher | Athabasca University Press (PUBLISHER • CA) |
| DOI | 10.19173/irrodl.v22i4.5401 |
| OpenAlex | W4213194184 |
| Language | EN |
| Citations received | 10 |
| References cited | 13 |
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 self-regulators. 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
Cluster analysis · Data science · Educational Data Mining · Hierarchical clustering · Machine learning · Mathematics education · Multilevel model · Self-regulated learning · Computer Science · Innovative Teaching and Learning Methods · Online Learning and Analytics · Psychology
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| Unique citing works | 10 |
|---|---|
| Citations per year | 5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 9 |