Employability Prediction of Information Technology Graduates using Machine Learning Algorithms

Abstract

The ability to predict graduates' employability to match labor market demands is crucial for any educational institution aiming to enhance students' performance and learning process as graduates' employability is the metric of success for any higher education institution (HEI). Especially information technology (IT) graduates, due to the evolving demand for IT professionals increased in the current era. Job mismatch and unemployment remain major challenges and issues for educational institutions due to the various factors that influence graduates' employability to match labor market needs. Therefore, this paper aims to introduce a predictive model using machine learning (ML) algorithms to predict information technology graduates' employability to match the labor market demands. Five machine learning classification algorithms were applied named Decision tree (DT), Gaussian Naïve Bayes (Gaussian NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). The dataset used in this study is collected based on a survey given to IT graduates and employers. The performance of the study is evaluated in terms of accuracy, precision, recall, and f1 score. The results showed that DT achieved the highest accuracy, and the second highest accuracy was achieved by LR and SVM.

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“…Therefore, the targeted results can be binary results such as employability: [Employable or unemployable], or even multi-classification models that correspond to the labor market situation according to different approaches (SVM, ANN, LR, AdaBoost, etc.) [25].…”

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