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Recommender Systems for Technology Enhanced Learning

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Cover of 'Recommender Systems for Technology Enhanced Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Collaborative Filtering Recommendation of Educational Content in Social Environments Utilizing Sentiment Analysis Techniques
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    Chapter 2 Towards Automated Evaluation of Learning Resources Inside Repositories
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    Chapter 3 A Survey on Linked Data and the Social Web as Facilitators for TEL Recommender Systems
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    Chapter 4 The Learning Registry: Applying Social Metadata for Learning Resource Recommendations
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    Chapter 5 A Framework for Personalised Learning-Plan Recommendations in Game-Based Learning
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    Chapter 6 An Approach for an Affective Educational Recommendation Model
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    Chapter 7 The Case for Preference-Inconsistent Recommendations
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    Chapter 8 Further Thoughts on Context-Aware Paper Recommendations for Education
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    Chapter 9 Towards a Social Trust-Aware Recommender for Teachers
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    Chapter 10 ALEF: From Application to Platform for Adaptive Collaborative Learning
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    Chapter 11 Two Recommending Strategies to Enhance Online Presence in Personal Learning Environments
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    Chapter 12 Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem
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    Chapter 13 COCOON CORE: CO-author REcommendations Based on Betweenness Centrality and Interest Similarity
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    Chapter 14 Scientific Recommendations to Enhance Scholarly Awareness and Foster Collaboration
Attention for Chapter 10: ALEF: From Application to Platform for Adaptive Collaborative Learning
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Citations

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28 Dimensions

Readers on

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45 Mendeley
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2 CiteULike
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Chapter title
ALEF: From Application to Platform for Adaptive Collaborative Learning
Chapter number 10
Book title
Recommender Systems for Technology Enhanced Learning
Published by
Springer New York, March 2014
DOI 10.1007/978-1-4939-0530-0_10
Book ISBNs
978-1-4939-0529-4, 978-1-4939-0530-0
Authors

Mária Bieliková, Marián Šimko, Michal Barla, Jozef Tvarožek, Martin Labaj, Róbert Móro, Ivan Srba, Jakub Ševcech

Editors

Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Olga C. Santos

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 2%
Slovenia 1 2%
Brazil 1 2%
Unknown 42 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 16%
Student > Ph. D. Student 6 13%
Professor > Associate Professor 5 11%
Student > Bachelor 4 9%
Student > Postgraduate 3 7%
Other 11 24%
Unknown 9 20%
Readers by discipline Count As %
Computer Science 24 53%
Social Sciences 3 7%
Linguistics 1 2%
Unspecified 1 2%
Business, Management and Accounting 1 2%
Other 1 2%
Unknown 14 31%