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Predicting Students’ Academic Performance using Artificial Neural Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 19 |
| Year of Publication: 2023 |
| Authors: Solomon Tok Dung, Godwin Thomas, Yinka Oyerinde |
10.5120/ijca2023922889
|
Solomon Tok Dung, Godwin Thomas, Yinka Oyerinde . Predicting Students’ Academic Performance using Artificial Neural Networks. International Journal of Computer Applications. 185, 19 ( Jun 2023), 1-7. DOI=10.5120/ijca2023922889
Abstract
Currently, within our contemporary society and the world at large, it is next to impossible not to find technology in almost all sectors of human life, the educational sector inclusive. As part of policies in any educational institution, improving students’ academic performance is paramount amongst other expectations. In order to accomplish this, most secondary schools rely on the manual method of assumption and MOCK exams to evaluate students’ progress with the view of improving their outcome. This approach has been used over the years and it is evident that it has proven to be ineffective and inefficient. The proof can be seen in the continuous dwindling on the WAEC results of students in both private and public secondary schools in Nigeria. Now, despite the availability of Information and Communication Technology (ICT) and also students’ data, most secondary schools do not use the potentials embedded in the data available to look for a solution to this problem. Given existing approach(es) limitations to improving students' academic performance, the need for a better approach arises. Thus, this research proposes an approach that leverages on AI technology to realize better results. The research focuses on the use of ICT and Artificial Neural Networks to aid in predicting students' academic performance. By employing both qualitative and quantitative approaches the requirements for such a system were identified. WEKA was used as the simulation environment to test the proposed algorithm premised on serial factors identified. The test result premised on a prototype, shows that the model can be used to predict students' academic performance in secondary schools.
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