Evaluation of the academic performance of students’ using artificial neural network: Case study of Faculty of Engineering, Nigeria Maritime University, Okerenkoko, Delta State.

Registration ID: IJNRD_181899

Published ID: IJNRD2207003

DOI: Click Here to Get

Iwekumo Stevyn Akosubo , Bolou, D. Bolou , Kabiru Ahmed , Olateju, O. Abideen

Student performance, University Education, Data mining, Artificial Neural Network

The substantial development in electronic data for university students’ academic performance in either supervised or unsupervised learning has resulted in some meaningful information extracted from large volumes of data using diverse data mining techniques. Due to the increase in the rate of poor outcomes, the need to design a system that helps to reduce the menace of students’ poor academic performance or having to drop out of school was analysed. The purpose of this study was to develop a system to predict student performance with an Artificial Neutral Network approach to the predictive classification of students in the full range of academic performance (GPA) so as to determine which is more efficient in and in what case one should be preferred over the other, as well as to identify and understand the importance of the variables for each level (low, middle and high) of expected GPA. Artificial Neural networks often need a greater collection of observations to achieve enough predictive ability. The ANN is a suitable model for the prediction of students' academic performance in their final year under conditions of a very complex and great amount of data, in which a large number of variables interact in various complexes. The results attained during this study will allow the identification of the precise influence of every input set of variables on different levels of educational performance (high and low performance), on one hand, and customary processes across all students, on the opposite hand. Additionally, we identified which key factors had an important influence on overall students’ performance. Data were collected from the scholars of the school of Engineering, Nigeria Maritime University, Okerenkoko, Delta State. The study achieved an accuracy of over 92.3 percent, showing Artificial Neural Network's potential effectiveness as a predictive tool for accessing students’ academic performance.

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Iwekumo Stevyn Akosubo, Bolou, D. Bolou, Kabiru Ahmed, & Olateju, O. Abideen (July-2022). Evaluation of the academic performance of students’ using artificial neural network: Case study of Faculty of Engineering, Nigeria Maritime University, Okerenkoko, Delta State.. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(7), 18-25. https://ijnrd.org/papers/IJNRD2207003.pdf

Volume 7 Issue 7, July-2022

Pages : 18-25

Paper Reg. ID: IJNRD_181899

Published Paper Id: IJNRD2207003

Downloads: 000121986

Research Area: Computer Science & Technology 

Country: Okerenkoko, Delta, Nigeria

Published Paper PDF: https://ijnrd.org/papers/IJNRD2207003.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2207003

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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