Personalized Online Learning: Context Driven Massive Open Online Courses

The success of MOOC (massive open online courses) is rapidly increasing. Most educational institutions are highly interested in these online platforms, which embrace intellectual and educational objectives and provide various opportunities for lifelong learning. However, many limitations, such as learners' diversity, lack of motivation, affected learners' outcomes, which unfortunately raised the dropout rate. Thus, multiple solutions were afforded on MOOC platforms to tackle these common problems. This paper suggests a model outline of a customizable system Context-Driven Massive Open Online Courses that could be implemented in any learning environment, and that goes hand in hand with learners' context to boost their motivation towards learning, and to help identify their learning needs. The paper introduces CD-MOOC following a learner-based approach by employing two types of users' data; long-term and short-term data assembled form learners' online traces when interacting on the platform. The data help users design their own learning path based on their context and preferences.