Normalização de dispositivos móveis no processo de aprendizagem de inglês : um estudo à luz da complexidade

We live in a world inherently influenced by technology and in which education is immersed in realities made possible by the support of digital technologies, such as electronic mobile devices. Thus, the general aim of this study lies in mapping and analysing the influence of mobile devices on teaching, especially with reference to learning the English language. The specific aims are to investigate how the use of mobile devices is present in the research participants’ practices, consider whether such use is beneficial, according to the students, to the English language learning as well as mapping how the use of mobile devices favours the normalisation stage, taken in this research as a complex process.The theoretical background of this study includes the premises of the Paradigm of Complexity, especially concerning the acquisition of a second language, as well as the precepts of Normalisation, which is related to the total integration of digital technologies into the English teaching and learning process in such a way that they become invisible, and the theories of language learning mediated by computers and mobile devices. Methodologically, this is an ethnographic qualitative research and its context is a language institute located in the Triângulo Mineiro region. In addition to students from five groups in the institution, two teachers and an administrative assistant participated in the survey. Data was collected through an online questionnaire, learning reports produced by students and interviews with teachers and administrative staff. The analyses indicate that mobile devices are present in the daily practices of English learners, but these uses, in most cases, are carried out through the teacher's encouragement. Moreover, despite having positive sayings on the role of digital technologies in the process of English teaching and learning, there is, among students and teachers, a dichotomy between saying and doing about the learning contexts considered valid. Additionally, the use of mobile devices in the English learning process is not yet established as a normalised issue because the process of integration of technology in teaching is still ruled by traditional uses of the technology. I conclude that the use of mobile devices in the English learning process is still not normalised, because even if students use their mobile devices every day, they generally do not realize the affordances of such use as possibilities to learn English.

Uma abordagem baseada em filtragem colaborativa integrada a mapas de saliência para a recomendação de imagens

Nowadays, the amount of customers using sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. The sites, differently from physical stores, can make anything available to customers. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of Collaborating Filtering (CF), whose main underlying idea is that products are recommended to a given user based on purchase information and evaluations of past, by a group of users similar to the user who is requesting recommendation. One of the main challenges faced by such a technique is the need of the user to provide some information about her preferences on products in order to get further recommendations from the system. When there are items that do not have ratings or that possess quite few ratings available, the recommender system performs poorly. This problem is known as new item cold-start. In this paper, we propose to investigate in what extent information on visual attention can help to produce more accurate recommendation models. We present a new CF strategy, called IKB-MS, that uses visual attention to characterize images and alleviate the new item cold-start problem. In order to validate this strategy, we created a clothing image database and we use three algorithms well known for the extraction of visual attention these images. An extensive set of experiments shows that our approach is efficient and outperforms state-of-the-art CF RS.