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Enhancing Computer Students’ Academic Performance through Predictive Modelling - A Proactive Approach

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dc.contributor.author Mutanu, Leah
dc.contributor.author Machoka, Philip
dc.date.accessioned 2021-06-14T15:09:04Z
dc.date.available 2021-06-14T15:09:04Z
dc.date.issued 2019-09-23
dc.identifier.uri http://erepo.usiu.ac.ke/11732/6557
dc.description A Journal article by Dr. Leah Mutanu, a Lecturer in Information Systems Technology at the School of Science and Technology in USIU- Africa Full Article: https://doi.org/10.1109/ICCSE.2019.8845452 en_US
dc.description.abstract Through the application of ICT for predictive analytics, proactive strategies can be implemented to improve the quality of education for a country's development. The study demonstrates the process of predictive modelling of students' academic performance with a view of identifying strategies that can manage performance drivers. Machine learning algorithms such as Decision trees, Regression and Neural Networks were used in the research for prediction modelling. The results showed that students' performance can be modelled and predicted with reasonable accuracy that can inform strategies for improving performance. In order to improve the approach, the study recommends scaling the approach to make use of other algorithms, ICT tools, other degree programmes and incorporate other institutions. en_US
dc.publisher IEEE en_US
dc.subject Computer Studies en_US
dc.subject Academic Performance en_US
dc.subject Data Analytics en_US
dc.subject Predictive Modeling en_US
dc.subject Early Warning Systems en_US
dc.title Enhancing Computer Students’ Academic Performance through Predictive Modelling - A Proactive Approach en_US
dc.type Article en_US


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