Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems

Hanan Abdullah Mengash

doi:10.1109/access.2020.2981905

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 pre-admission criterion that most accurately predicts future student performance. Therefore, this score should be assigned more weight in admissions systems. We also found that the Artificial Neural Network technique has an accuracy rate above 79%, making it superior to other classification techniques considered (Decision Trees, Support Vector Machines, and Naive Bayes).

Highlights

Today, all higher education institutions, especially computer and engineering colleges, face challenges in the admissions process
This study focuses on supporting universities in making admissions decisions through the application of data mining techniques to better predict applicants’ academic performance before admission
2) To answer the second question in this study (Is it possible to predict applicants’ early academic performance before admitting them based on their pre-admission test scores?), we developed four prediction models by applying four wellknown data mining classification techniques, namely: Artificial Neural Network (ANN), Decision Tree, Support Vector Machine (SVM), and Naive Bayes

Summary

Introduction

All higher education institutions, especially computer and engineering colleges, face challenges in the admissions process. Each university should use the best possible techniques for predicting applicants’ future academic performance before admitting them. This would support university decision makers as they set efficient admissions criteria. Most higher education institutions face challenges when they analyze their large educational databases to predict students’ performance [1]. This is because they use only conventional statistical methods rather than new and efficient predictive techniques such as Educational Data Mining (EDM), which is the most popular technique to

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