The document compares three different recommender strategies for personal learning environments (PLEs). The strategies include: 1) A federated search widget that aggregates resources from different repositories and provides recommendations based on a user's search history and interests. 2) A community-based recommender that allows users to voluntarily share PLE experiences and generates recommendations based on clustered activity patterns and popular items. 3) A psycho-pedagogical recommender developed according to learning models that provides recommendations based on a user's goals and competencies as assessed through questionnaires.

8. 3. Comparison of our PLE recommenders 11-13/07/2011 PLE Conference,Southampton, 2011 /9 , © role-project.eu Binocs widget PLEShare PP recommender recommender strategy CF, PageRank-like & content-based CF & IR/clustering (cliques, topics, ...) rule/profile-based (competences) data & data gathering on entering search terms, automated tagged bookmarks, voluntarily shared questionnaires, automated (profile) estimated accuracy high (works well in specialized scope; fallback through IR) average (requires ‘initialization’, cf. cold start & sparsity) average (rules and profile must be given) PLE scenario support & usability average (PLE design phase not considered) good (currently only focus on PLE design); usable prototypes good; restricted to pre-def. domains; no cold-start problem privacy concerns sufficient anonymization privacy statement, anonymized activity recordings (=patterns) raw usage data not used; user profiles not addressed yet preliminary experiences preferences for Google results; uptake in business setting better three studies; works but requires pilot users sharing patterns (e.g. teachers) internal evaluations; efforts to integrate new data; requires modelling expertise