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

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.

Mobile e-Services

“…Research cases also show other areas for the use of recommender systems, e.g. when developing services for mobile learning; the service recommends learning objects by taking into account the students' interests and collective preferences (Cazella, Barbosa, Reategui, Behar & Acosta, 2014).…”