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Adaptive Multimedia Retrieval: First International Workshop, by Gareth J. F. Jones (auth.), Andreas Nürnberger, Marcin PDF

By Gareth J. F. Jones (auth.), Andreas Nürnberger, Marcin Detyniecki (eds.)

ISBN-10: 3540221638

ISBN-13: 9783540221630

ISBN-10: 3540259813

ISBN-13: 9783540259817

This e-book is a longer choice of contributions that wereoriginally subm- ted to the first overseas Workshop on Adaptive Multimedia Retrieval (AMR 2003), which used to be equipped as a part of the twenty sixth German convention on Arti?cial Intelligence (KI 2003),and held in the course of September 15–18,2003at the college of Hamburg, Germany. prompted via the general good fortune of the workshop – as printed by way of the stimulating surroundings throughout the workshop and the variety of very and lively contributors – we ?nally made up our minds to edit a publication in line with revised papers that have been at the beginning submitted to the workshop. moreover, we invited a few extra introductory contributions with the intention to have the ability to supply a conclusive e-book on present subject matters within the quarter of adaptive multimedia retrieval structures. we are hoping that we have been capable of prepare a stimulating choice of articles for the reader. we adore to thank the organizationcommittee of the twenty sixth German convention on Arti?cial Intelligence (KI 2003) for offering the environment and the admin- trative aid in figuring out this workshop as a part of their software. specially, we love to thank Christopher Habel for selling the workshop as a part of the convention software and Andreas Gun ¨ ther for his style help during the association process.

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Additional resources for Adaptive Multimedia Retrieval: First International Workshop, AMR 2003, Hamburg, Germany, September 15-16, 2003, Revised Selected and Invited Papers

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We are following a somewhat different approach. We are trying to build user models of each student in the class. We take a very broad definition of a user model as a set of beliefs about the user [6]. For example, in a course on user interface design, we would like to build a model of how well the learner knows each component of the course. The design of such a model will be defined by the long term purpose for which the user model is needed. As we have already noted, one of the long term goals is to personalise teaching.

Unlike a conventional face-to-face lecture, the student can stop the lecture at any time, replaying an element that they did not quite follow. Students can return to the lecture or a particular slide in it, as they need, for example, when tackling an assignment, working on a tutorial task or preparing for final examinations. As more learning resources become available on-line, the log data about their accesses becomes a rich source of user modelling information. Each access can be regarded as a form of evidence that the student has progressed in their learning.

IEEE Transactions on Circuits and Video Technology 8 (1998) 644–655 Overview of Approaches to Semantic Augmentation 33 3. : A vector space model for information retrieval. Journal of the American Society for Information Science 18 (1975) 613–620 4. : Relevance feedback information retrieval. : The Smart retrieval system experiments in automatic document processing. PrenticHall (1971) 313–323 5. : Content-based query of image databases: inspirations from text retrieval. K. Ersboll, P. Johansen, Eds.

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Adaptive Multimedia Retrieval: First International Workshop, AMR 2003, Hamburg, Germany, September 15-16, 2003, Revised Selected and Invited Papers by Gareth J. F. Jones (auth.), Andreas Nürnberger, Marcin Detyniecki (eds.)


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