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Educational Recommender Systems and Technologies: Practices and Challenges

Educational Recommender Systems and Technologies: Practices and Challenges

Copyright: © 2012 |Pages: 362
ISBN13: 9781613504895|ISBN10: 1613504896|EISBN13: 9781613504901
DOI: 10.4018/978-1-61350-489-5
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MLA

Santos, Olga C., and Jesus G. Boticario. Educational Recommender Systems and Technologies: Practices and Challenges. IGI Global, 2012. https://doi.org/10.4018/978-1-61350-489-5

APA

Santos, O. C. & Boticario, J. G. (2012). Educational Recommender Systems and Technologies: Practices and Challenges. IGI Global. https://doi.org/10.4018/978-1-61350-489-5

Chicago

Santos, Olga C., and Jesus G. Boticario. Educational Recommender Systems and Technologies: Practices and Challenges. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-489-5

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Recommender systems have shown to be successful in many domains where information overload exists. This success has motivated research on how to deploy recommender systems in educational scenarios to facilitate access to a wide spectrum of information. Tackling open issues in their deployment is gaining importance as lifelong learning becomes a necessity of the current knowledge-based society. Although Educational Recommender Systems (ERS) share the same key objectives as recommenders for e-commerce applications, there are some particularities that should be considered before directly applying existing solutions from those applications.

Educational Recommender Systems and Technologies: Practices and Challenges aims to provide a comprehensive review of state-of-the-art practices for ERS, as well as the challenges to achieve their actual deployment. Discussing such topics as the state-of-the-art of ERS, methodologies to develop ERS, and architectures to support the recommendation process, this book covers researchers interested in recommendation strategies for educational scenarios and in evaluating the impact of recommendations in learning, as well as academics and practitioners in the area of technology enhanced learning.

Table of Contents

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Front Materials
Title Page
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Copyright Page
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Editorial Advisory Board
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Foreword
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Preface
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Chapters
Knowledge Modeling for the Recommendation Process
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Techniques, Algorithms and Architectures to Support the Recommendation Process
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Chapter 3
Real World E-Learning Scenarios
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Challenges for Educational Recommender Systems
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Back Materials
About the Contributors
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Index
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