Abstract
<p class="0abstract">For an innovation producing education, MOOC (Massive Open Online Course) platforms offer a plethora of learning resources and pedagogical activities to support the university’s 4.0 new era and the lifelong learning movement. Nevertheless, the rapid advances in learning technologies imply the need for personalized guidance for learners and adapted learning materials. In this paper we seek to enhance the MOOC learner experience by providing a semantic recommender system for the diversity and abundance of MOOCs available for learners. Firstly, the paper analyses the state of the art of the semantic recommendation approach in a distance learning context. Then it describes the proposed MOOC recommendation system that uses the ontological representation of the learner model and MOOCs content to make its intelligent suggestions. Finally, we explore the development phases of the semantic MOOC recommendation system to define the implications for the progress of our research.</p>
Highlights
Seeking an intuitive experience in online learning has become the new trend in lifelong learning platforms like MOOC platforms by exploiting the plethora of possibilities offered by artificial intelligence
We discuss in the second section the knowledgebased recommender system features for the distance learning “to suggest adequate MOOCs for specific learner models”
Semantic recommender systems rely on the characteristics of learners as a knowledge source to predict the most suitable MOOCs for the learner’s preferences and needs
Summary
Seeking an intuitive experience in online learning has become the new trend in lifelong learning platforms like MOOC platforms by exploiting the plethora of possibilities offered by artificial intelligence In this context, the university 4.0 uses artificial intelligence for a better understanding of learning needs by learning analytics tools; and monitoring of learners' progress by using adaptive learning toolkits. “This complexity in recommendation of learning resources can be alleviated by personalizing the learner profile to match the needs and characteristics of the learner by using knowledge structures such as ontology” [3] For this purpose, we discuss in the second section the knowledgebased recommender system features for the distance learning “to suggest adequate MOOCs for specific learner models”. As for the third section, we design the functional architecture of our proposed RS for MOOCs, the use cases of its main actors: the learner and the MOOC provider, and : the development phases of our ongoing research
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