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A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 27 |
| Year of Publication: 2022 |
| Authors: Anietie Ekong, Abasiama Silas, Saviour Inyang |
10.5120/ijca2022922340
|
Anietie Ekong, Abasiama Silas, Saviour Inyang . A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network. International Journal of Computer Applications. 184, 27 ( Sep 2022), 44-49. DOI=10.5120/ijca2022922340
Abstract
Student admission’s process is a method of selecting qualified candidates for admission. Challenges such as facility constraints and insufficient ability to meet the continuously rising needs of post-secondary education. There is still an absorption capacity problem in some parts of the world as the growing number of students applying for admission for post-secondary education far surpasses the rate of expansion and this makes the selection process to be a daunting tasks. In this study, Artificial Neural network (ANN) was adopted for the determination of admissibility of candidates for post-secondary education based on (O’level Results, CGPA (Cumulative Grade Point Average), Departmental Rank (DPR) etc. Results indicated effective prediction based the performance analysis using the Confusion Matrix and AUC -ROC and gave a 99% accuracy on the dataset.
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