Recommender Systems for Technology Enhanced Learning: Research Trends & Applications

Nikos Manouselis (Editor), Katrien Verbert (Editor), Hendrik Drachsler (Editor), Olga C. Santos (Editor)

    Research output: Book/ReportBook editingProfessional

    Abstract

    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.
    Original languageEnglish
    PublisherSpringer
    Number of pages306
    Edition1
    ISBN (Electronic)978-1-4939-0530-0
    ISBN (Print)978-1-4939-0529-4, 978-1-4939-4656-3
    DOIs
    Publication statusPublished - 2014

    Keywords

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

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