Recommender Systems for Technology Enhanced Learning

Research Trends and Applications

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

Table of contents

  1. Front Matter
    Pages i-xiv
  2. User and Item Data

    1. Front Matter
      Pages 1-1
    2. Cristian Cechinel, Sandro da Silva Camargo, Salvador Sánchez-Alonso, Miguel-Ángel Sicilia
      Pages 25-46
    3. Stefan Dietze, Hendrik Drachsler, Daniela Giordano
      Pages 47-75
  3. Innovative Methods and Techniques

    1. Front Matter
      Pages 97-97
    2. Ioana Hulpuş, Conor Hayes, Manuel Oliveira Fradinho
      Pages 99-122
    3. Olga C. Santos, Jesus G. Boticario, Ángeles Manjarrés-Riesco
      Pages 123-143
    4. Christina Schwind, Jürgen Buder
      Pages 145-157
    5. Tiffany Y. Tang, Pinata Winoto, Gordon McCalla
      Pages 159-173
  4. Platforms and Tools

    1. Front Matter
      Pages 175-175
    2. Soude Fazeli, Hendrik Drachsler, Francis Brouns, Peter Sloep
      Pages 177-194
    3. Mária Bieliková, Marián Šimko, Michal Barla, Jozef Tvarožek, Martin Labaj, Róbert Móro et al.
      Pages 195-225
    4. Samuel Nowakowski, Ivana Ognjanović, Monique Grandbastien, Jelena Jovanovic, Ramo Šendelj
      Pages 227-249
    5. Alejandro Fernández, Mojisola Erdt, Ivan Dackiewicz, Christoph Rensing
      Pages 251-265
    6. Rory L. L. Sie, Bart Jan van Engelen, Marlies Bitter-Rijpkema, Peter B. Sloep
      Pages 267-282
    7. Jan Petertonkoker, Wolfgang Reinhardt, Junaid Surve, Pragati Sureka
      Pages 283-306

About this book

Introduction

As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.

Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.

Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.

Keywords

Adaptive web systems e-learning experimental simulations information retrieval personalization real-world implementations recommender systems state-of-the-art technology enhanced learning web-based systems

Editors and affiliations

  • Nikos Manouselis
    • 1
  • Hendrik Drachsler
    • 2
  • Katrien Verbert
    • 3
  • Olga C. Santos
    • 4
  1. 1.Agro-KnowAthensGreece
  2. 2.Faculty of Psychology and Educational SciencesWelten Institute – Research Centre for Learning, Teaching and Technology, Open University of the NetherlandsHeerlenThe Netherlands
  3. 3.Department of Computer ScienceVUB & KU LeuvenLeuvenBelgium
  4. 4.aDeNu Research Group, Artificial Intelligence DepartmentComputer Science School, UNEDMadridSpain

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-0530-0
  • Copyright Information Springer Science+Business Media New York 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4939-0529-4
  • Online ISBN 978-1-4939-0530-0
  • About this book
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