Open AccessPredicting Students' Academic Performance Using Artificial Neural Network: A Review of Intrinsic and Extraneous Factors
Author(s) -
Akinwale Mayomi Aisida,
AUTHOR_ID
Publication year - 2021
Publication title -
advances in multidisciplinary and scientific research journal
Language(s) - English
Resource type - Journals
ISSN - 2488-8699
DOI - 10.22624/aims/abmic2021-v2-p20
Subject(s) - artificial neural network , artificial intelligence , computer science , session (web analytics) , machine learning , range (aeronautics) , academic achievement , mathematics education , psychology , engineering , world wide web , aerospace engineering
Students’ academic performance is a measure of how the student has performed for a period in the academic parlance. It can be measured as semester-based, session-based or throughout the duration of the course of study. In recent time research, Artificial Neural Network (ANN) has been used globally, there are successful implementations in a wide range of classification, and prediction to be more efficient compared with other classifiers. Predictions are aimed at forecasting what might happen in the future by the means of estimating the likelihood of a certain events that has already occurred. We review literatures on the use of ANN for Predicting Students' Academic Performance Using Artificial Neural Network and also explore programming languages of choice for same. A number of intrinsic and extrinsic factors affecting students’ performance were also identified Keywords: Review, Data Mining, Approaches, Prediction, Factors, Students' Academic Performance, Artificial Neural Network