In the learning process, there are some elements that have to be noticed in order to make it effective and optimal. The elements are; the objective of the learning process, the type of content, the learning method, the delivery media, and the learning style of the students. The focus of this research was the learning style of the students, in the learning context with interactive multimedia (an...

AIM To determine the predominant learning style preferences of undergraduate nursing students. METHOD A demographic questionnaire and Honey and Mumford's (2000a) learning styles questionnaire were administered to a purposive sample of 136 students. RESULTS A response rate of 81% (110) was obtained. The results are congruent with U.K. studies, which show that the reflector is the preferred l...

We gradually develop a novel functional for joint variational object segmentation and shape matching. The formulation, based on the Wasserstein distance, allows modelling of local object appearance, statistical shape variations and geometric invariance in a uniform way. For learning of class typical shape variations we adopt a recently presented approach and extend it to support inference of de...

In designing learning materials there is often the assumption that all trainees will learn in a similar manner. This approach ignores the important issue of individual differences in cognitive style. Cognitive style may be defined as an individual’s consistent approach to organising and processing information during thinking. Style does not appear to be related to intelligence and reflects qual...

How does the machine classify styles in art? And how does it relate to art historians’s methods for analyzing style? Several studies have shown the ability of the machine to learn and predict style categories, such as Renaissance, Baroque, Impressionism, etc., from images of paintings. This implies that the machine can learn an internal representation encoding discriminative features through it...

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning...