Guia didático-metodológico para aprendizagem e memorização dos conhecimentos disseminados nos moocs – Massive Open Online Courses

The present research has character exploratory, bibliographic and qualitative. It is based in consolidated scientific arguments in cognitive theories inspired in constructivist method and, under this perspective proposes to develop a didactic guide oriented to students of courses MOOCs - Massive Open Online Courses that will make it possible to maximize the utilization and the assimilation of the knowledge available in these courses. Intends also prepare these students in practice of a methodology of storage that enables the knowledge acquired are not lost nor be forgotten over the course of time. The theoretical framework, based on the theories of Meaningful Learning (Ausubel), the Genetic Epistemology (Piaget), Socioconstructivist (Vigotsky) and the Multimedia Learning (Mayer), subsidizes the understanding of important concepts such as meaningful learning, previous knowledge, and conceptual maps. Supported by fundamental contribution of the Theory of Categories, which are inter-related to concepts applicable to teaching methodology supported by use of structured knowledge maps in the establishment of the binomial teaching-learning; and with valuable study performed by teachers Luciano Lima (UFU) and Rubens Barbosa Filho (UEMS) that culminated with the development of Exponential Effective Memorization Method in Binary Base (Double MEB).

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.