An educational data mining model for promoting self-regulated learning on learning management systems
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Date
2023
Authors
Eric, Araka N.
Journal Title
Journal ISSN
Volume Title
Publisher
Kenyatta University
Abstract
Online learning environments such as Learning Management Systems (LMS) utilized by institutions of higher learning to deliver open and distance education have different features that may not adequately provide individualized support to learners. Online learners experience inadequate instructor support as many students are enrolling in fully online or blended courses. Therefore, the success of online learning depends on the learner’s ability to take control of their learning process as defined by the Self-Regulated Learning (SRL) theory. Since online learners have no restricted time to go online and learn, the existing freedom requires students who can take charge and control their learning. Moreover, existing models for supporting SRL do not link SRL in educational psychology and SRL in LMS. In view of this, it is necessary to have LMS that support SRL by providing targeted interventions based on students’ online learning activities data. This study explored the use of Educational Data Mining (EDM) techniques to support students’ SRL skills on LMS. To accomplish this, the research applied a mixed-methods approach that involved three phases. First, qualitative and quantitative methods were used in problem identification. Qualitative research was used to explore the current methods used to measure and promote SRL in online learning environments. Quantitative research was used to conduct a pre-study to establish how LMS features are utilized in teaching and learning in higher institutions of learning in Kenya. Secondly, Integrated Design Process was used to design clustering and classification algorithms and integrated them into Moodle LMS to analyze students’ learning activities data. Finally, true experiment research was used to establish the effectiveness of the developed EDM model in promoting SRL through sampled University students who enrolled in data science with python course for 12 weeks. This study, therefore, contributes an EDM model that contains algorithms derived from learning theories as applied in self-regulated learning. Results indicate that students were able to progress from poor self-regulators cluster level to good self-regulators and exemplary self-regulators. The results reveal that students were able to evolve from one cluster to another over time as a result of receiving EDM interventions during the online course. The findings demonstrate that providing external support through the use of EDM prompts leads to the growth of students’ SRL skills. Moreover, this study reveals that it is possible to measure and promote SRL concurrently using EDM techniques. Future studies can focus on the evaluation of the EDM model under varied contextual conditions to examine the effect of SRL interventions on students’ SRL skills and academic performance.
Description
A thesis submitted in partial Fulfillment of the Requirements for the Award of the Degree of Doctor of Philosophy (Computer Science) in the School of Engineering and Architecture of Kenyatta University
March, 2023
Keywords
data mining model, self-regulated learning, Learning management systems