OPT-BAG Model for Predicting Student Employability

Abstract:

The use of machine learning to predict student employability is important in order to analyse a student’s capability to get a job. Based on the results of this type of analysis, university managers can improve the employability of their students, which can help in attracting students in the future. In addition, learners can focus on the essential skills identified through this analysis during their studies, to increase their employability. An effective method called OPT-BAG (OPTimisation of BAGging classifiers) was therefore developed to model the problem of predicting the employability of students. This model can help predict the employability of students based on their competencies and can reveal weaknesses that need to be improved. First, we analyse the relationships between several variables and the outcome variable using a correlation heatmap for a student employability dataset. Next, a standard scaler function is applied in the preprocessing module to normalise the variables in the student employability dataset. The training set is then input to our model to identify the optimal parameters for the bagging classifier using a grid search cross-validation technique. Finally, the OPT-BAG model, based on a bagging classifier with optimal parameters found in the previous step, is trained on the training dataset to predict student employability. The empirical outcomes in terms of accuracy, precision, recall, and F1 indicate that the OPT-BAG approach outperforms other cutting-edge machine learning models in terms of predicting student employability. In this study, we also analyse the factors affecting the recruitment process of employers, and find that general appearance, mental alertness, and communication skills are the most important. This indicates that educational institutions should focus on these factors during the learning process to improve student employability.