Predicting Post-Internship Employability Using Ensemble Machine Learning Approach
Keywords:
graduate employability, machine learning, internship, career readiness, employability prediction,, ensemble methodsAbstract
Graduate employability is crucial for both students and higher education institutions. While academic performance has traditionally been a key predictor of employability, its predictive power is limited, necessitating the exploration of additional factors influencing post-internship job placement. This study investigates the impact of internship-related variables on graduate employability, such as duration, training performance, and prior work experience. Employing a machine learning approach on a dataset comprising student records from Universiti Malaysia Sarawak spanning from 2019 to 2021, we compared the performance of various algorithms, including ensemble methods. Feature selection and repeated K-fold cross-validation optimised model performance. Results indicate that stacking outperforms traditional models, achieving an accuracy of 91%. Particularly, internship duration and training performance emerged as significant predictors of employability. These findings underscore the importance of robust internship programs in enhancing graduate outcomes. Future research could explore the competencies developed during internships and their correlation with job success.
References
Baker, I., & Fitzpatrick, D. (2022). Student Experiences in Pre-COVID Virtual Internships: Integration, Barriers, Motivation Challenges, Supportive Supervisors, and Intern Growth. American Journal of Distance Education, 36(2), 90–102. https://doi.org/10.1080/08923647.2022.2034399
Casuat, C. D., Festijo, E. D., & Alon, A. S. (2020). Predicting students’ employability using support vector machine: a smote-optimized machine learning system. International Journal of Emerging Trends in Engineering Research, 8(5), 2101-2106. https://doi.org/10.30534/ijeter/2020/102852020
Del Rio Rajanti, S. (2024). Global perspective in career development: The influence of international experiences on post-graduation employment (Bachelor's thesis, Tampere University of Applied Sciences). https://urn.fi/URN:NBN:fi:amk-2024051712981
ElSharkawy, G., Helmy, Y., & Yehia, E. (2022). Employability prediction of information technology graduates using machine learning algorithms. International Journal of Advanced Computer Science and Applications, 13(10), 359-367. https://urn.fi/URN:NBN:fi:amk-2024051712981
Grillo, J. L. (2023). Students’ Perceptions of the Effects of Internships on Confidence and Post-Graduation Outcomes in Higher Education (Doctoral dissertation, St. John's University). https://scholar.stjohns.edu/theses_dissertations/553
Hassock, L., & Hill, C. (2022). Employability and employment: The role of higher education in a rapidly changing world. In Higher education and job employability (pp. 155-178). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-05716-8_8
Haque, R., Quek, A., Ting, C. Y., Goh, H. N., & Hasan, M. R. (2024). Classification Techniques Using Machine Learning for Graduate Student Employability Predictions. International Journal on Advanced Science, Engineering & Information Technology, 14(1), 45-56. https://doi.org/10.18517/ijaseit.14.1.19549
Herbert, I. P., Rothwell, A. T., Glover, J. L., & Lambert, S. A. (2020). Graduate employability, employment prospects and work-readiness in the changing field of professional work. The International Journal of Management Education, 18(2), 100378. https://doi.org/10.1016/j.ijme.2020.100378
Kim, Y.-A., Kim, K.-A., & Tzokas, N. (2022). Entrepreneurial universities and the effect of the types of vocational education and internships on graduates’ employability. Studies in Higher Education, 47(5), 1000–1009. https://doi.org/10.1080/03075079.2022.2055324
Margaryan, S., Saniter, N., Schumann, M., & Siedler, T. (2022). Do Internships Pay Off? Journal of Human Resources, 57 (4), 1242-1275. https://doi.org/10.3368/jhr.57.4.0418-9460R2
Ministry of Higher Education (2021). Graduate Tracer Study Microdata. https://great.mohe.gov.my/penerbitan/LAPORAN%20KAJIAN%20PENGESANAN%20GRADUAN%20TVET%202020.pdf.
Nisha, S. M., & Rajasekaran, V. (2018). Employability skills: A review. IUP Journal of Soft Skills, 12(1), 29–37. https://ssrn.com/abstract=3251255
Oberman, W., Hunt, I., Taylor, R. K., & Morrisette, S. (2021). Internships and occupational self-efficacy: Impact and gender differences. Journal of Education for Business, 96(7), 424–434. https://doi.org/10.1080/08832323.2020.1848768
Perusso, A., & Baaken, T. (2020). Assessing the authenticity of cases, internships and problem-based learning as managerial learning experiences: Concepts, methods and lessons for practice. The International Journal of Management Education, 18(3), 100425. https://doi.org/10.1016/j.ijme.2020.100425
Rogers, S. E., Miller, C. D., Flinchbaugh, C., Giddarie, M., & Barker, B. (2021). All internships are not created equal: Job design, satisfaction, and vocational development in paid and unpaid internships. Human Resource Management Review, 31(1), 100723. https://doi.org/10.1016/j.hrmr.2019.100723
Saidani, O., Menzli, L. J., Ksibi, A., Alturki, N., & Alluhaidan, A. S. (2022). Predicting student employability through the internship context using gradient boosting models. IEEE Access, 10, 46472-46489. https://doi.org/10.1109/ACCESS.2022.3170421
Tamrat, W. (2023). The shifting landscape of graduate employment in Ethiopia: changes, challenges and responses. Policy Reviews in Higher Education, 7(2), 211-228. https://doi.org/10.1080/23322969.2023.2196552
Vo, M. T., 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
Webb, M. E., & Paretti, M. (2022, October). Exploring Professional identity development research on displaced higher education students. In 2022 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). https://doi.org/10.1109/FIE56618.2022.9962604
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