nite-context sources from fractal

questions about the evolution of topics in the LAK Dataset to distill knowledge about past, present and future of LAK research. 2. In Section 5 we describe the visual analytics application D-VITA 2, which puts the toolkit to answer the

the learning technologies metadata in the Conceptual Graph knowledge representation formalism, and uses related inference techniques to provide advanced, personalized functionality. CG-PerLS allows learning resource creators to manifest their material, client-side learners to access these resources in a way

Abstract: Major objective of the web-based learning is to design customized courseware taking into account of the input knowledge level and the specific objective of each individual learner. In order to improve the design of adaptive dynamic courseware to meet the requirement of individual learner

search is necessary for macros. The present paper demonstrates these facts in simple list manipulation problems and by proving geometric theories. 1 Introduction Neural networks are superior to knowledge representation due to their natural learning ability. However, in contrast to pattern recognition

the es-sential changes in a visual scenery in a condensed way such that a robot can recognize and learn a manipula-tion without prior object knowledge. To achieve this we continuously track image segments in the video and construct a dynamic graph sequence. Topologi-cal transitions of those graphs occur

Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, several studies have addressed the issue of reusing the knowledge acquired in solving previous related tasks

, especially in contexts where visual representations have traditionally been subordinate to linguistic and numerical representations. Keywords: visualization, problem-solving, communication, explanation, active learning When we understand something, we say that we "see" it. We arrive at the solution

JAQ is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language ~Java!, although it can be easily extended to any other topic. DynJAQ integrates case-based knowledge into a graph-based representation that can be easily learned

pictures, visual simulations, 2D and 3D visualizations, graphs, and illustrations. Pictures are superior to words for remembering concrete concepts The cognitive theory presented by Mayer (2002) offers three theory-based assumptions about how people learn from words and pictures. The first assumption