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st. Mary's University Institutional Repository St. Mary's University Institutional Repository

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/6882
Title: Prediction of Students’ Academic Performance Using Machine Learning: The Case of Wachemo University
Authors: Asefa, Girma, FikaduWayesa
Keywords: Prediction, Machine Learning, Classification and Performance
Issue Date: 21-Aug-2021
Publisher: St.Mary's University
Abstract: An educational institution must always have an estimated previous knowledge of enrolled students to predict their performance in future academics. The comparative analysis of results helped the weaker students to improve their passing which has eventually led to increased overall passing average of the course. In this research, the data was analyzed using classification models with mixing the internal and external data sources, and then compared the model to select the best prediction model that produced highest accuracy, which helped the university to identify the students likely to fail, and work on their academics accordingly in order to achieve better results. Accuracy of this classification algorithm is compared in order to check best performance. K-NN achieved the best prediction results, which are very satisfactory compared to those of similar approaches in both classification.
URI: http://hdl.handle.net/123456789/6882
Appears in Collections:The 9th Annual Open and Distance Education Seminar

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