Content´s Personalized Recommendation for Implementing Ubiquitous Learning in Health 2.0

This paper proposes a content recommendation mechanism as part of a model for implementing ubiquitous learning for supporting people with chronic diseases who are treated at home, so that they can learn more about treatments for their disease. The proposed approach is supported by the Situated Learning Theory, in which learning takes place based on day-to-day activities and real situations. In this case, the model supports the development of tools that can learn about the user´s context, based on data obtained via sensors installed on users or in their home, as well as data supplied directly by the user interface of their mobile devices, and data provided by the healthcare team, and, after that, recommend contents about their diseases.