Recommender Systems for Technology Enhanced Learning: Research Trends and Applications - Rilegato

Rilegato

ISBN 10: 1493905295 ISBN 13: 9781493905294

Casa editrice: Springer-Nature New York Inc, 2014

Riassunto

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.

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Recensione

From the book reviews:

“Book represents a collection of state-of-the-art contributions devoted to RSs for TEL and explores contemporary research achievements in the area. ... This very interesting, well-timed volume will provide great opportunities for PhD students and newcomers to this field to continue with high-quality research efforts. The book is also interesting for master’s students who would like to acquire adequate knowledge and emergent research achievements in this field. Secondary school teachers and experienced researchers could also find this book useful and interesting.” (M. Ivanović, Computing Reviews, October, 2014)

Contenuti

Collaborative Filtering Recommendation of Educational Content in Social Environments utilizing Sentiment Analysis Techniques.- Towards automated evaluation of learning resources inside repositories.- Linked Data and the Social Web as facilitators for TEL recommender systems in research and practice.- The Learning Registry: Applying Social Metadata for Learning Resource Recommendations.- A Framework for Personalised Learning-Plan Recommendations in Game-Based Learning.- An approach for an Affective Educational Recommendation Model.- The Case for Preference-Inconsistent Recommendations.- Further Thoughts on Context-Aware Paper Recommendations for Education.- Towards a Social Trust-aware Recommender for Teachers.- ALEF: from Application to Platform for Adaptive Collaborative Learning.- Two Recommending Strategies to enhance Online Presence in Personal Learning Environments.- Recommendations from Heterogeneous Sources in a Technology Enhanced Learning Ecosystem.- COCOON CORE: CO-Author Recommendations based on Betweenness Centrality and Interest Similarity.- Scientific Recommendations to Enhance Scholarly Awareness and Foster Collaboration.

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 1493905295
• ISBN 13: 9781493905294
• Rilegatura: Copertina rigida
• Numero di pagine: 320
• Redattore: Manouselis Nikos, Drachsler Hendrik, Verbert Katrien, Santos Olga C.
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania