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<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>

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The phenomenon of high dropout rates has been the concern of MOOC providers and educators since the emergence of this disruptive technology in online learning. This led to the focus on learner motivation studies from different aspects: demotivation signs detection, learning path personalization, course recommendation, etc.  Our paper aims to predict learner motivation for MOOCs to select the right MOOC for the right learner. So, we predict the motivation in an educational data mining approach by extracting and preprocessing learners' navigation traces on a MOOC platform and building a machine learning model that predicts accurately a given learner motivation for a MOOC. The comparison of the performance of four supervised learning algorithms resulted in the selection of the random forest classifier as a modeling technique for motivation prediction. Afterward, the Machine Learning-based recommendation function was tested for learners of the MOOC platform dataset to recommend the Top-10 MOOCs suitable for the target learner. Finally, further research on learner characteristics considered in recommender systems could enlarge the recommendation scope of MOOCs and maintain learner motivation.