DATA MINING TECHNIQUES TO PREDICT STUDENT PERFORMANCE IN DECISION MAKING

Abstract

An admissions system based on valid and reliable admissions criteria is very important to select
candidates likely to perform well academically at institutions of higher education. This study focuses on ways to
support universities in admissions decision making using data mining techniques to predict applicants' academic
performance at university. A data set of 2,039 students enrolled in a Computer Science and Information College
of a Saudi public university from 2016 to 2019 was used to validate the proposed methodology. The results
demonstrate that applicants' early university performance can be predicted before admission based on certain
pre-admission criteria (high school grade average, Scholastic Achievement Admission Test score, and General
Aptitude Test score). The results also show that Scholastic Achievement Admission Test score is the preadmission
criterion that most accurately predicts future student performance. Therefore, this score should be
assigned more weight in admissions systems. We also found that the Arti_cial Neural Network technique has an
accuracy rate above 79%, making it superior to other classi_cation techniques considered (Decision Trees,
Support Vector Machines, and Naïve Bayes).