Utilizing Machine Learning to Develop Cloud-Based Apprenticeship Programs Aligned with Labor Market Demands
Osama Hosam, Rasha Abousamra, Ahmed Said Ghonim, Khaled F. Shaalan · 2023
There is a potential disparity between academic pursuits and labor market requirements. We proposed an approach exploring the feasibility of leveraging machine learning to tailor apprenticeship programs and enhance learning outcomes. The proposed approach involves the collaboration of four key stakeholders, namely, the employer, trainer, university management, and apprentice, each with unique roles in the apprenticeship program. A machine learning algorithm is employed to customize Occupational Learning Outcomes (OLO) for each job, with the use of blockchain technology to facilitate the student credit system. The entire system is hosted on a cloud-based centralized database to enable dynamic and sustainable program modification. The paper concludes by highlighting the potential of digital technologies to transform apprenticeships and create new opportunities and challenges.