A Data-Driven Profile-based Analytics for Career Path and Upskilling Recommendations in HRS

Authors

  • Paul Nigel S. Abalos , Thelma D. Palaoag,

Keywords:

profile-based analytics, upskilling recommendations, career path prediction, sequential neural network

Abstract

This quantitative-experimental research aimed to develop a profile-based data analytics module for career path and upskilling recommendations module for the Data-Driven Human Resource Analytics System (DDHRAS) of the largest state university in Ilocos Region and address common analytics results heavily based on skills which do not fully include other useful data in some sectors, such as the academe in which the researchers belong to. Hence, the study is motivated to come up with an analytics module that is close to the university HRS by incorporating significant employee personal data, educational background, seminars and trainings, skills and competencies, board examinations, job performance score, work experience, and tenure. A scoring matrix based on human resource policies was devised and paired with Word2vec algorithm to evaluate which profile composition criteria each employee has an advantage of or needs to improve to qualify for promotions. Dataset covers 628 personnel data sheets of employees for School Years 2020 to 2023 from different campuses of the subject institution. The sequential neural network to predict career path and upskilling recommendations was utilized and resulting models were evaluated using model classification performance metrics. Experimental results show that the models developed have consistently obtained an average accuracy of 88.99% during performance validation, indicative of rigid classification performance. The implication of the developed models, when fully integrated and implemented within the existing system of the HRD, can provide an alternative faster career path and upskilling recommendations to lessen the burden of HRD evaluators, thus, save valuable resources.

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References

Abu-El-Haija, S., & Al-Khateeb, A. (2022). “Research methods in machine learning: A content analysis.” International Journal of Computer and Information Technology, 11(1), 1-12.

Berra, A., & Sanna, L. D. S. (2022). The evolving role of HR in the digital age: Challenges and opportunities. International Journal of Human Resource Management, 33(8), 2051-2074. DOI: 10.1080/09548799.2019.1604414.

Brewster, C., & Larsen, H. H. (2021). The strategic role of HR in a changing world: A critical review. Human Resource Management Journal, 31(2), 507-534. DOI: 10.1111/hrmj.12413.

Fan, Y., Lu, Y., & Zhao, X. (2020). A machine learning approach to career path prediction and upskilling recommendation in the IT industry. Journal of Business Analytics, 2(2), 189-202. DOI: 10.1080/24756820.2020.1730411.

Karakurum, F., Gupta, A., & Raj, A. (2022). Leveraging machine learning for personalized upskilling recommendations. Human Resource Management, 61(3), 829-854. DOI: 10.1002/hrm.22235.

Lawler, E. E., & Mohrman, S. A. (2019). The future of work and the role of HR: Challenges and opportunities. Human Resource Management, 58(12), 1804-1824. DOI: 10.1002/hrm.22061.

Liu, Y., Shi, Y., & Zhao, P. (2022). A personalized recommendation system for upskilling and career development. Decision Support Systems, 151, 113588. DOI: 10.1016/j.dss.2022.113588

Narkhede, S. (2023). Understanding Confusion Matrix. Towards Data Science. Web article: https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62.

Nighania, K. (2018). Various Ways to Evaluate a Machine Learning Model’s Performance. Towards Data Science, https://towardsdatascience.com/various-ways-to-evaluate-a-machine-learning-models-performance-230449055f15.

Romero, R., López, M. H., & Arnau, J. (2021). Machine learning for skill gap analysis and recommendation in the learning and development context. Studies in Educational Evaluation, 73, 100670. DOI: 10.1016/j.stueduc.2021.100670

Schuler, R. S., & Jackson, S. E. (2021). The changing landscape of HR analytics: A strategic perspective. Organizational Dynamics, 50(1), 101314. DOI: 10.1016/j.orgdyn.2020.101314.

Seth, N. (2023). Estimation of Neurons and Forward Propagation in Neural Net. Analytics Vidhya. Web article: https://www.analyticsvidhya.com/blog/2021/04/estimation-of-neurons-and-forward-propagation-in-neural-net/.

Singh, A., & Singh, P. K. (2021). Profile-based career path recommendation system using machine learning techniques. Artificial Intelligence and Machine Learning in Healthcare, 20(1), 1-14. DOI: 10.1007/978-981-15-7406-9_1

Tzafrir, S., Baruch, Y., & De Beer, J. (2020). HR 4.0: The next-generation human resources model. Journal of Management Studies, 57(1), 1-31. DOI: 10.1111/joms.12434.

Xu, Y., Zhao, P., & Li, J. (2020). A profile-driven recommendation system for career path planning and upskilling in the big data era. International Journal of Human Resource Management, 31(10), 1502-1532. DOI: 10.1080/09548799.2018.1455271

Yang, M., Chen, B., & Zhou, M. (2023). Towards an AI-powered HR system for career development and upskilling. International Journal of Human Resource Management, 34(2), 307-334. DOI: 10.1080/09548799.2021.1975650.

Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series (IOP Science, 2019). DOI: 10.1088/1742-6596/1168/2/022022

Zhang, Y., Chen, L., & Zhao, P. (2023). Explainable recommendation systems for upskilling and career development. Decision Support Systems, 152, 113610. DOI: 10.1016/j.dss.2023.113610.

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Published

26.03.2024

How to Cite

Paul Nigel S. Abalos ,. (2024). A Data-Driven Profile-based Analytics for Career Path and Upskilling Recommendations in HRS. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 2935–2945. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5945

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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