  Many researchers have studied student academic performance in supervised and
unsupervised learning using numerous data mining techniques. Neural networks
often need a greater collection of observations to achieve enough predictive
ability. Due to the increase in the rate of poor graduates, it is necessary to
design a system that helps to reduce this menace as well as reduce the
incidence of students having to repeat due to poor performance or having to
drop out of school altogether in the middle of the pursuit of their career. It
is therefore necessary to study each one as well as their advantages and
disadvantages, so as to determine which is more efficient in and in what case
one should be preferred over the other. The study aims to develop a system to
predict student performance with Artificial Neutral Network using the student
demographic traits so as to assist the university in selecting candidates
(students) with a high prediction of success for admission using previous
academic records of students granted admissions which will eventually lead to
quality graduates of the institution. The model was developed based on certain
selected variables as the input. It achieved an accuracy of over 92.3 percent,
showing Artificial Neural Network potential effectiveness as a predictive tool
and a selection criterion for candidates seeking admission to a university.

Computers and Society;Artificial Intelligence;Machine Learning