1.

Record Nr.

UNINA9910437922803321

Titolo

Recommender systems for learning / / Nikos Manouselis ... [et al.]

Pubbl/distr/stampa

New York, : Springer, c2013

ISBN

1-283-61174-0

9786613924193

1-4614-4361-X

Edizione

[1st ed. 2013.]

Descrizione fisica

1 online resource (84 p.)

Collana

SpringerBriefs in electrical and computer engineering

Altri autori (Persone)

ManouselisNikos

Disciplina

006.33

Soggetti

Educational technology

Recommender systems (Information filtering)

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references.

Nota di contenuto

Introduction and Background -- TEL as a recommendation context -- Survey and Analysis of TEL Recommender Systems -- Challenges and Outlook.

Sommario/riassunto

Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.