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Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments

Sílvio César Cazella, Jorge Luiz Victória Barbosa, Eliseo Berni Reategui, Patricia Alejandra Behar, Otavio Costa Acosta
Copyright: © 2014 |Pages: 21
ISBN13: 9781466645424|ISBN10: 1466645423|EISBN13: 9781466645431
DOI: 10.4018/978-1-4666-4542-4.ch007
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MLA

Cazella, Sílvio César, et al. "Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments." Technology Platform Innovations and Forthcoming Trends in Ubiquitous Learning, edited by Francisco Milton Mendes Neto, IGI Global, 2014, pp. 120-140. https://doi.org/10.4018/978-1-4666-4542-4.ch007

APA

Cazella, S. C., Barbosa, J. L., Reategui, E. B., Behar, P. A., & Acosta, O. C. (2014). Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments. In F. Neto (Ed.), Technology Platform Innovations and Forthcoming Trends in Ubiquitous Learning (pp. 120-140). IGI Global. https://doi.org/10.4018/978-1-4666-4542-4.ch007

Chicago

Cazella, Sílvio César, et al. "Recommending Academic Papers for Learning Based on Information Filtering Applied to Mobile Environments." In Technology Platform Innovations and Forthcoming Trends in Ubiquitous Learning, edited by Francisco Milton Mendes Neto, 120-140. Hershey, PA: IGI Global, 2014. https://doi.org/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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