Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment

The evolution of the Web 2.0 and the intensive use of peer-to-peer networks allow us to access more and more information from disparate data sources than in the past, thus making life-long learning more effective. In this scenario, a critical issue still remains to be addressed: the reliability of resources, whether they can be recommended as useful and the reliability of peers, whether it is possible to trust them as providers. We propose to integrate these concepts with e-learning, proposing a model for searching for personalised and useful learning paths suggested by reliable (trusted) peers. We performed simulations on the Merlot data set enhanced with information extracted from Advogato, Epinions and Ariadne data sets, testing the efficiency and effectiveness of the proposed approach.