Context-aware authoring and presentation from open e-learning repository

With the explosive growth in the World Wide Web over the past few decades, a predominant part of the pedagogical arena is making a transition from stereotype textbook learning to massive open online learning. Efforts are being made to develop and foster crowd sourced massive open repositories of learning objects, which can be tapped to author courses for diverse learners with varied backgrounds dynamically. Developing systems to author and deliver such courses has been of rising importance to contemporary researchers and this paper proposes an efficient context-aware open e-learning environment to do the same. The learning objects having high aptness to the particular course and high content-based predicted rating pertaining to the particular learner´s preferences are picked from the open repository and the course structure is modeled using communicating dynamic Petri nets. Ratings and feedback from the user are obtained during presentation, based on which the course delivery is made adaptive. Rating prediction through Collaborative filtering is used for this purpose. The ratings are also used to implicitly learn the learner´s preferences and to establish an aptness score for each learning object.