Controlling Employability Issues of Computing Science Graduates through Machine Learning-Based Detection and Identification

Abstract

The unemployment rate of graduate students in the area of computing is tremendously growing. One of the main reasons is the difference between the acquired skills from universities and the skills required from industry which is looking for potential graduates who can work in the digitally transforming framework of today’s society. Many studies have been conducted to emphasize the issue of unemployment utilizing traditional approaches. However, these methods are time-consuming and difficult to bring into effect, while involving a lot of effort, which had no definite influence or impact on the studies to date. Hence, this study proposes a predictive artificial intelligent model through the use of a conceptual framework called Intelligent Collaborative Framework, addressing the gap between university computing graduates and the industry needs. This model is achieved via machine learning classifiers to recognize the issue and solve the problem between university computing graduates' and employers’ expectations. In addition, the study identifies the required skills for computing graduate students to be employed in the industry. Several experiments were conducted using a dataset gathered from two computing departments and through a survey done among the graduates. The experiment results show that the ADA, SVM, and LR outperform the other classifiers. The model performance accuracy reached 89% for F1-Score. In addition, the best features (computing and training courses) were identified using the SelectKBest. The mutual information gain can assist in quickly obtaining jobs.