Open Access
ARTICLE
OPT-BAG Model for Predicting Student Employability
Minh-Thanh Vo1, Trang Nguyen2, Tuong Le3,4,*
1 Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam
2 Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
3 Laboratory for Artificial Intelligence, Institute for Computational Science and Artificial Intelligence, Van Lang
University, Ho Chi Minh City, Vietnam
4 Faculty of Information Technology, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam
* Corresponding Author: Tuong Le. Email:
Computers, Materials & Continua 2023, 76(2), 1555-1568. https://doi.org/10.32604/cmc.2023.039334
Received 23 January 2023; Accepted 18 May 2023; Issue published 30 August 2023
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.
Keywords
Cite This Article
APA Style
Vo, M., Nguyen, T., Le, T. (2023). OPT-BAG model for predicting student employability. Computers, Materials & Continua, 76(2), 1555-1568. https://doi.org/10.32604/cmc.2023.039334
Vancouver Style
Vo M, Nguyen T, Le T. OPT-BAG model for predicting student employability. Comput Mater Contin. 2023;76(2):1555-1568 https://doi.org/10.32604/cmc.2023.039334
IEEE Style
M. Vo, T. Nguyen, and T. Le "OPT-BAG Model for Predicting Student Employability," Comput. Mater. Contin., vol. 76, no. 2, pp. 1555-1568. 2023. https://doi.org/10.32604/cmc.2023.039334