Recomendação Personalizada de Conteúdo para Implementação de Aprendizagem Ubíqua em Saúde 2.0 (Content's Personalized Recommendation for Implementing Ubiquitous Learning in Health 2.0)

Francisco Milton Mendes Neto (, Alisson Alan Lima da Costa (, Enio Lopes Sombra (, Jonathan Darlan Cunegundes Moreira (, Ricardo Alexsandro de Medeiros Valentim (, Jose Javier Samper Zapater (, Rogério Patrício Chagas do Nascimento (, Cecília Dias Flores (

1Universidade Federal Rural do Semi-Árido (UFERSA)
2Universidade Federal do Rio Grande do Norte (UFRN)
3Universitat de Valencia (UVEG)
4Universidade Federal de Sergipe (UFS)
5Fundação Universidade Federal de Ciências da Saúde de Porto Alegre (FUFCSPA)

This paper appears in: Revista IEEE América Latina

Publication Date: Dec. 2014
Volume: 12,   Issue: 8 
ISSN: 1548-0992

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.

Index Terms:
Ubiquitous Learning, Health 2.0, Home Care, Content Recommendation Systems, User Profile   

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