Reliable personalized learning paths: The contribution of trust to e-Learning

Abstract

The personalization of learning paths according to personal profiles is one of the major advantages of computer assisted learning. However, the choice of most suitable learning resources is sometime a controversial question due to different e-learning providers assessment about each other and their learning resources. In this work an approach to address this issue is presented, by exploiting the idea of trustworthiness associated to both learning objects as well as to peers in a P2P e-learning scenario. We test our proposal on an e-learning network based on MERLOT and ARIADNE data. Results show the effectiveness of trust in e-learning context.