Using a hybrid approach to recommender systems, we propose the development of a system that leverages the benefits of three different techniques: probabilistic topic models, collaborative tagging systems and domain ontologies. The dataset to be used for the experiments are educational learning materials, and meta-data information of the interaction of learners with the learning materials. The key contributions of the proposed research is the development of a recommender system that that supports Technology Enhanced Learning (TEL) in the following ways: (a) for each item a user interacts with, provide finer grained recommendations that would facilitate the understanding of the item being read, (b) incorporate a tag analysis model that takes into consideration the meta-cognitive activities of a user while interacting with an item, and (c) offers improved learning experience by utilizing a navigational sequence model that adapts to the users learning styles and prior knowledge.