Using Learning Analytics to Predict Students’ Performance in Moodle Learning Management System: A Case of Mbeya University of Science and Technology.

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dc.contributor.authorMwalumbwe, Imani
dc.contributor.authorMtebe, Joel S.
dc.date.accessioned2025-03-25T11:47:41Z
dc.date.available2025-03-25T11:47:41Z
dc.date.issued2017
dc.descriptionThis Journal Article of Information and Communication Technology was Published by EJISDC.
dc.description.abstractThe past decade has seen the rapid adoption and use of various Learning Management Systems (LMS) in Africa, and Tanzania in particular. Institutions have been spending thousands of dollars to implement these systems in a bid to improve the quality of education as well as increasing students’ enrolments through distance and blended learning. However, the impact of these system on improving students’ performance has been a popular subject of research in recent years. Studies have been relying on data from users’ opinions and subjective interpretation through surveys to determine the effectiveness of LMS usage on students’ learning performance. The use of such data is normally subject to the possibility of distortion or low reliability. Therefore, this study designed and developed Learning Analytics tool and used the tool to determine the causation between LMS usage and students’ performance. Data from LMS log of two courses delivered at Mbeya University of Science and Technology (MUST) were extracted using developed Learning Analytics tool and subjected into linear regression analysis with students’ final results. The study found that discussion posts, peer interaction, and exercises were determined to be significant factors for students’ academic achievement in blended learning at MUST. Nonetheless, time spend in the LMS, number of downloads, and login frequency were found to have no significant impact on students’ learning performance. The implications of these results on improving students’ learning are discussed.
dc.description.sponsorshipMbeya University of Science and Technology.
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/10.1002/j.1681-4835.2017.tb00577
dc.identifier.urihttps://repository.must.ac.tz/handle/123456789/302
dc.language.isoen
dc.publisherEJISDC
dc.titleUsing Learning Analytics to Predict Students’ Performance in Moodle Learning Management System: A Case of Mbeya University of Science and Technology.
dc.typeArticle
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