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

Robert Oboko,Rhoda Gitonga,Eric Araka,Elizaphan Maina

doi:10.19173/irrodl.v22i4.5401

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 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.

Highlights

The increased adoption of technology to enhance learning along a continuum that ranges from physical classrooms to online learning has opened valuable opportunities for decision makers in institutions of learning
We present a review of the literature on current Educational data mining (EDM) techniques used to group learners into various self-regulated learning (SRL) profiles according to their behavioral interactions in online learning environments
The findings demonstrate that agglomerative hierarchical clustering is the best performing algorithm

Summary

Introduction

The increased adoption of technology to enhance learning along a continuum that ranges from physical classrooms to online learning has opened valuable opportunities for decision makers in institutions of learning. The identification of SRL profiles in online learning has been based mostly on data collected using student self-report tools (Barnard et al, 2010; Broadbent & Fuller-Tyszkiewicz, 2018; Valle et al, 2008; Yot-Domínguez & Marcelo, 2017). Learners often may fail to recall the strategies they use during learning as self-report tools are employed before or after the learning process (Broadbent & Fuller-Tyszkiewicz, 2018; Elsayed et al, 2019). Educational data mining (EDM) techniques are likely to measure and profile learners more accurately as compared to self-report tools, as they use actual datasets collected from online learning environments. With EDM techniques being part of machine learning algorithms, there is a need for an empirical analysis to establish the optimal values of parameters and the best algorithm to use with educational data

Methods

Results

Discussion

Conclusion