Classification Techniques Using Machine Learning for Graduate Student Employability Predictions

Abstract

The issue of employability has gained significant importance, not only for graduate students but also for higher educational institutions. In this regard, employability prediction models using machine learning have emerged as crucial techniques for assessing students' potential to secure employment after graduation. Enhancing university graduate employability is critical because student unemployment is a global concern that has widespread negative effects on both individuals and institutions. Therefore, focusing on graduate employability predictions using machine learning techniques is considered essential in addressing this issue. Traditionally, demographic and academic attributes, such as CGPA, have been considered key factors in determining student employment status. However, research suggests that various other factors, such as student satisfaction, might influence employability. This study employs machine learning techniques to identify the factors that affect graduate student employability. The objective is to investigate the features significantly influencing students' ability to secure employment. Data was collected from Malaysia's Ministry of Education's graduate tracer study (SKPG). Several classification algorithms were applied, including Logistic Regression, Random Forest, Naïve Bayes, Support Vector Machine, Extreme Gradient Boosting, and Artificial Neural Networks (ANN). The results show that ANN achieved the highest accuracy, with around 80%. The findings also revealed that student demographic and academic features and student satisfaction level with the university facilities (e.g., library and counseling service) are considered significant for graduate student employability predictions. Consequently, the empirical results can help higher educational institutions enhance facilities and prepare students with the necessary skills for future employability.