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A recommendation model on personalised learning to improve the user ’ s performance and interaction in MOOCs and OERs

Current models and methodologies in the field of educational technology are lacking in engagement between formal and informal learning. Usually, only activity within a formal environment (i.e., assignments, grades, et cetera) is stored, tracked and retrieved as an input parameter in recommender systems. However, there is normally one issue missing: a useful combination with the informal activity of every user (e.g., social networks, continuous evaluation). In addition, tutoring systems in academic domains are usually based only on content filtering and collaboration from other students, which we believe contribute to dissolving the crucial role of the tutor. Last, OERs, MOOCs and SPOCs have become a crucial part of combined educational models, between formal and informal settings, where they play a key role in the learning path of every user. Thanks to the user’s behaviour and the user’s interactions, efficient monitoring and personalized counselling by a tutor, we can improve the learning performance of every user. In this paper, we show a specific implementation of LIME, a personalized elearning model for restricted social networks and learning management systems, which supports this approach, specifically for massive courses and large data sets. Furthermore, we present the practical application of the model into a prototype (iLIME), which has been developed to demonstrate how our model could operate independently of the learning management infrastructure in use. This application case is developed under the Sakai LMS, in the context of a MOOC strategy to be implemented at university level. We provide pedagogical and technical issues, challenges and solutions, developed in order to run iLIME and deliver LIME-based recommendations to learners in a real academic scenario, focused on open educational resources and massive courses.