Research Article

A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network

by  Anietie Ekong, Abasiama Silas, Saviour Inyang
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Issue 27
Published: Sep 2022
Authors: Anietie Ekong, Abasiama Silas, Saviour Inyang
10.5120/ijca2022922340
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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

                        @article{ 10.5120/ijca2022922340,
                        author  = { Anietie Ekong,Abasiama Silas,Saviour Inyang },
                        title   = { A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network },
                        journal = { International Journal of Computer Applications },
                        year    = { 2022 },
                        volume  = { 184 },
                        number  = { 27 },
                        pages   = { 44-49 },
                        doi     = { 10.5120/ijca2022922340 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2022
                        %A Anietie Ekong
                        %A Abasiama Silas
                        %A Saviour Inyang
                        %T A Machine Learning Approach for Prediction of Students’ Admissibility for Post-Secondary Education using Artificial Neural Network%T 
                        %J International Journal of Computer Applications
                        %V 184
                        %N 27
                        %P 44-49
                        %R 10.5120/ijca2022922340
                        %I Foundation of Computer Science (FCS), NY, USA
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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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Machine Learning Neural Network Model Prediction Student’s Admission.

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