The gap between the Labor market needs and recent Higher education graduates continues to perpetually expand worldwide, including the Saudi labor market; where many employers complain that recent graduates lack some essential skills that would allow them to successfully compete in their workplace. On the other hand, online learning methods and technologies such as Massive Open Online Courses (MOOCs) are developing rapidly, providing an excellent mean for lifelong learning. However, due to the overwhelmingly large number of MOOCs, find it challenging to locate the most relevant courses to optimize their skill set. This research is inspired by the Saudi movement to improve education outcomes as an objective of Saudi 2030 Vision. This paper mainly inspects the ‘lost in hyperspace’ phenomenon plaguing MOOCs platforms and content. It proposes a hybrid, personalized MOOCs search model known as the MOOC Recommender Search Engine (MRSE). This model aims to help learners search and reach suitable high-quality courses, which increases their chances of meeting the labor market requirements.
MOOCs, Personalization, Search Engines, Recommender Systems, User Modeling (UM), MOOCs Recommendation Search Engine (MRSE)