A multilayer approach for recommending contextual learning paths

Nowadays, distance learning is achieved through new technological systems, which are able to give several advantages in the training process. Modern e-learning environments exploit technologies ca-pable of designing increasingly specific learning paths. This approach could be interesting in the field of Cultural Heritage. In this scenario, the introduction of a framework able to automatically design tailored learning paths to be used during the visit of archaeological sites could be engaging. The proposed framework aims to exploit contextual graph approaches, such as Ontology and Context Dimension Tree and probabilistic graph approaches such as Bayesian Networks for inferring adapted and contextual learning paths. In particular, it supports learners during their visits in real scenarios as archaeological parks or museums. This engine selects contents and services according to the learner’s profile and the context. The main advantage of the proposed system is to design and suggest tailored learning paths to be used on-site in order to improve the training process. Besides, the proposed approach can exploit context modelling and predictive techniques, which can improve the ability to recommend learning paths. A prototype has been developed and tested in real scenarios as the archaeological parks of Paestum, Herculaneum and Pompeii. In particular, several aspects have been tested, such as performance, usability and effectiveness, and more specific tests have been performed measuring the accuracy in learning path recommendations with promising results.