Learning resource repositories and libraries make educational material and/or its metadata available in digital format, the sharing of which is their core raison d’eˆtre. Their reuse has been touted for enabling cost savings because the creation of high quality material is costly, hence the focus on standards that enable interoperability (Campbell, 2003) even across repositories (Ternier et al., 2008). Traditionally, metadata and/or web directories are used for searching and exploring the content items. Currently, novel exploratory search systems are developed for learning resources to assist users in obtaining information to meet their information needs. Such systems include social navigation and collaborative recommender systems, both of which belong to the family of techniques called social information retrieval (Goh and Foo, 2007). Social navigation involves using the behaviour of other people to help navigate online. It is driven by the tendency of people to follow other people’s footprints when they feel lost (Dieberger et al., 2000). Such footprints in an online environment are what Claypool et al. (2001) define as implicit and explicit interest indicators and can be acquired either directly from the user (e.g. rating) or indirectly (e.g. time spent on an object). Collaborative recommender systems, on the other hand, use explicit ratings to find like-minded users (Adomavicius, 2005). Evaluation of recommender systems traditionally focuses on the algorithms and their performance (Herlocker et al., 2004), similar to exploratory search systems (White et al., 2008). Evaluating recommenders from the user perspective has received less attention (McNee, 2006). Within the field of technology enhanced learning (TEL) such systems exist. Rafaeli et al. (2005) introduced a system to harness the social perspectives in learning where the learner could choose from whom to take recommendations (friend or algorithm). Koper (2005) used indirect social interaction in choosing a path that allows successful competition of a learning task. Drachsler et al.(2008) took this research further showing that users employing a recommender system that offers navigation support in self-organised learning networks, were more efficient time-wise in completing an equal number of learning activities.