Content Personalized Recommendation Engine to Support an Informal Learning Environment in the Health Context

Content Personalized Recommendation Engine to Support an Informal Learning Environment in the Health Context

Alisson Alan Lima da Costa (Rural Federal University of Semi-Arid, Brazil), Francisco Milton Mendes Neto (Rural Federal University of Semi-Arid, Brazil), Enio Lopes Sombra (Rural Federal University of Semi-Arid, Brazil), Jonathan Darlan Cunegundes Moreira (Rural Federal University of Semi-Arid, Brazil), Rafael Castro de Souza (Rural Federal University of Semi-Arid, Brazil) and Jerffeson Gomes Dutra (Rural Federal University of Semi-Arid, Brazil)
DOI: 10.4018/978-1-5225-0125-1.ch019
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People with chronic diseases suffer with limitations imposed by their health condition and learn more about the disease helps in improving the quality of life. This is possible because the use in mass of mobile devices and the advent of Web 2.0 tools, which gave rise to the Health 2.0 concept. This search for the construction of knowledge by stimulating citizens to be active and responsible for their health. However, provide contextualized knowledge at the right time, it is not a trivial task due to the diversity of content and user's profiles. The solution to this is to provide informal learning through personalized recommendation of content by providing relevant content to users related to their health. This chapter proposes a personalized recommendation system of content, which includes the union of different recommendation techniques and genetic algorithm, seeking efficacy on the recommendation of the contents to people with chronic diseases aiming informal learning in health.
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Chronic diseases represent a high percentage of mortality in worldwide. However, most of them can be prevented and controlled to promote a better quality of life. Therefore, it is necessary for the individual know his disease, and then make the recommended treatment (with medicines, diets, healthy habits, changes in lifestyle and practice exercises) resulting in becoming an active and responsible for care in his own health.

There is a consensus that knowledge about the health condition brings to patients with chronic diseases, the ability to get along better with their diseases, the motivation to carry out the treatment and support for social coexistence. This knowledge can be obtained through health professionals but also through informal learning. According to Cross (2012), informal learning can be understood as the unofficial way, where most people learn to do their job. On this basis, it can be said that informal learning can happen through sharing experiences with other carriers of the disease, or over the internet, social networks, etc.

In this scenario, the concept of Health 2.0 becomes a motivating factor since it combines informal learning to the use of technologies and Web 2.0 tools (social networks, personal health records, blogs, videos, services, etc.) as a way of capture by user experience, information relating to health and, by the combination of these data and information, provide personalized care and content (Hughes, Joshi & Wareham, 2008). However, this learning needs to be directed to each user according to their needs and interests, and content recommendation engines can contribute to this process by analyzing the user profile and extracting information to recommend appropriate content to their situation.

As Barcellos et al. (2007), content recommendation engines are designed to support users in the search process on the Web, showing information and content to the user based on their profile and preferences. Thus, with the objetive to develop a learning environment in the context of Health 2.0 which best meets the needs of users and that seeks the improvement of the learning process, it is essential to consider aspects related to real situations of the user's daily life, transcending the barriers of formalism education.

The ubiquitous learning can support informal education providing a favorable environment for self-learning and user interactions with the real environment through experiences gained by social media. According to Saccol, Schlemmer and Barbosa (2010), the ubiquitous learning is defined as the use of mobile devices, wireless mobile communication technologies, sensors and location mechanism, in order to assist the educational process, taking into account specific characteristics of individual.

Based on the presented approaches, the developed recommendation engine was directed to two types of chronic diseases: (i) diabetes mellitus (DM), considered a matter of global public health since it affects a large number of people (WHO, 2013) and (ii) Amyotrophic Lateral Sclerosis, better known as ALS. According to Yamanaka et al. (2008) and Eisen (2009), ALS is a neurodegenerative disease characterized by a progressive and fatal loss of motor neurons of the cerebral cortex, brain stem and spinal cord, but the cognitive and intellectual activities remain intact.

Key Terms in this Chapter

Ontologies: They represent an explicit and formal specification of a shared concept of a domain of interest.

Ubiquitous Recommendation: It is the provision of personalized content that can occur anywhere and at any time.

Collaborators: People who have similar characteristics in terms of both profiles as the choices made about content.

Chronic Diseases: These diseases bring limitations to its bearers and thus affect the normal activities that these people perform day-to-day.

MobiLEHealth: informal learning environment on health which aims to monitor the actions of users and recommending content in a personalized manner to people with chronic diseases.

Personalized Recommendation of Contents: It is providing relevant content to the user, based on their profile that is, taking into account the particular characteristics of each user.

Informal Learning: It is the continuous process of acquisition of knowledge by an individual, which is responsible for their learning that occurs through their daily experiences.

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