79,795 research outputs found

    Learning style and learning strategies in a multimedia environment

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    There is a growing realization that it may be expeditious to combine elements from different theories of learning when trying to derive a coherent and usable policy towards computerā€mediated learning. Consideration of the subtle distinction between Computerā€Aided Learning (CAL) and Computerā€Aided Instruction (CAI) conform the basis of a possible classification of computerā€mediated learning, and hence of multimedia tools. This classification enables the development of a continuum upon which to place various strategies for computerā€mediated learning, and hence a means of broadly classifying multimedia learning tools

    Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

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    Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

    Video Data Visualization System: Semantic Classification And Personalization

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    We present in this paper an intelligent video data visualization tool, based on semantic classification, for retrieving and exploring a large scale corpus of videos. Our work is based on semantic classification resulting from semantic analysis of video. The obtained classes will be projected in the visualization space. The graph is represented by nodes and edges, the nodes are the keyframes of video documents and the edges are the relation between documents and the classes of documents. Finally, we construct the user's profile, based on the interaction with the system, to render the system more adequate to its references.Comment: graphic
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