Produção Nacional
Content's Personalized Recommendation for Implementing Ubiquitous Learning in Health 2.0
2014; Institute of Electrical and Electronics Engineers; Volume: 12; Issue: 8 Linguagem: Inglês
10.1109/tla.2014.7014522
ISSN1548-0992
AutoresFrancisco Milton Mendes Neto, Alisson Alan Lima da Costa, Ênio Lopes Sombra, Jonathan Darlan Cunegundes Moreira, Ricardo Alexsandro de Medeiros Valentim, J. Javier Samper, Rogério Patrício Chagas do Nascimento, Cecília Dias Flores,
Tópico(s)Multimedia Communication and Technology
ResumoThis 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.
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