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Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments
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Author(s): Sílvio César Cazella (Universidade Federal des Ciências da Saúde de Porto Alegre, Brazil & Universidade do Vale do Rio dos Sinos, Brazil), Jorge Luiz Victória Barbosa (Universidade do Vale do Rio dos Sinos, Brazil), Eliseo Berni Reategui (Universidade Federal do Rio Grande do Sul, Brazil), Patricia Alejandra Behar (Universidade Federal do Rio Grande do Sul, Brazil)and Otavio Costa Acosta (Universidade Federal do Rio Grande do Sul, Brazil)
Copyright: 2014
Pages: 21
Source title:
Technology Platform Innovations and Forthcoming Trends in Ubiquitous Learning
Source Author(s)/Editor(s): Francisco Milton Mendes Neto (Rural Federal University of Semi-Arid, Brazil)
DOI: 10.4018/978-1-4666-4542-4.ch007
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Abstract
Mobile learning is about increasing learners' capability to carry their own learning environment along with them. Recommender Systems are widely used nowadays, especially in e-commerce sites and mobile devices, for example, Amazon.com and Submarino.com. In this chapter, the authors propose the use of such systems in the area of education, specifically for the recommendation of learning objects in mobile devices. The advantage of using Recommender Systems in mobile devices is that it is an easy way to deliver recommendations to students. Based on this scenario, this chapter presents a model of a recommender system based on information filtering for mobile environments. The proposed model was implemented in a prototype aimed to recommend learning objects in mobile devices. The evaluation of the received recommendations was conducted using a Likert scale of 5 points. At the end of this chapter, some future works are described.
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