Exploiting trust into e-learning: adding reliability to learning paths

The personalisation 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. In particular, trust relationships among peers allows to select which ones of them can be considered more authoritative in answering a query within a given topic (described by shared ontologies), whereas trust about learning objects allows to select most reliable resources. 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.

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