This paper presents the design, implementation and some original features of a Web-based learning environment STyLE (Scientific Terminology Learning Environment). STyLE supports adaptive learning of English terminology with a target user group of non-native speakers. It attempts to improve Computer-Aided Language Learning (CALL) by intelligent integration of Natural Language Processing (NLP) an...

Several approaches to teaching undergraduate physics and engineering students using both the Dunn and Dunn and the Kolb Learning Style Models are discussed. The Dunn and Dunn Learning Style Model is employed with nonmajors enrolled in introductory physics at American University and the Kolb Learning Style Model is employed with freshman engineering students at Purdue University. The basic eleme...

BACKGROUND Learning styles are cognitive, emotional, and physiological traits, as well as indicators of how learners perceive, interact, and respond to their learning environments. According to Honey-Mumford, learning styles are classified as active, reflexive, theoretical, and pragmatic. OBJECTIVE The purpose of this study was to identify the predominant learning styles among pharmacy studen...

We introduce a novel bilinear boosting algorithm, which extends the multi-class boosting framework of JointBoost to optimize a bilinear objective function. This allows style parameters to be introduced to aid classification, where style is any factor which the classes vary with systematically, modeled by a vector quantity. The algorithm allows learning to take place across different styles. We ...

The goal of computational music modeling is to construct models that capture the structure of music. We present our work on learning style-specific models for the rhythmic structure on a single line of music. The task by which the model is evaluated is to predict the duration of the next note given the sequence of durations leading to that note. We construct several different models that we tra...

Being a cross-camera retrieval task, person reidentification suffers from image style variations caused by different cameras. The art implicitly addresses this problem by learning a camera-invariant descriptor subspace. In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation. CamStyle can serve as a data augmentation approach that smooths the camer...

Researchers agree that it is possible to diagnose student’s learning style and that learners with a dominant preference for certain learning style may have difficulties in knowledge acquisition in conditions where teaching strategy is not compatible with it. This paper presents an experimental work conducted in order to determine if students with different learning styles have different navigat...

BACKGROUND The term "learning style" refers to the fact that each person has a different way of accumulating knowledge. While some prefer listening to learn better, others need to write or they only need to read the text or see a picture to later remember. According to Fleming and Mills the learning styles can be classified in Visual, Auditory and Kinesthetic. There is no evidence that teaching...