Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements fo...

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Detalles Bibliográficos
Autores principales: Erdt, Mojisola, Fernández, Alejandro, Rensing, Christoph
Formato: Articulo
Lenguaje:Inglés
Publicado: 2015
Materias:
Informática
Evaluation
K Computing Milieux
K.3 Computers and Education
K.3.1 Computer Uses in Education
K.3.1.d Distance learning
Recommender Systems
Survey
Technology Enhanced Learning
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/86675
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-86675
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Informática
Evaluation
K Computing Milieux
K.3 Computers and Education
K.3.1 Computer Uses in Education
K.3.1.d Distance learning
Recommender Systems
Survey
Technology Enhanced Learning
spellingShingle Informática
Evaluation
K Computing Milieux
K.3 Computers and Education
K.3.1 Computer Uses in Education
K.3.1.d Distance learning
Recommender Systems
Survey
Technology Enhanced Learning
Erdt, Mojisola
Fernández, Alejandro
Rensing, Christoph
Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
topic_facet Informática
Evaluation
K Computing Milieux
K.3 Computers and Education
K.3.1 Computer Uses in Education
K.3.1.d Distance learning
Recommender Systems
Survey
Technology Enhanced Learning
description The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.
format Articulo
Articulo
author Erdt, Mojisola
Fernández, Alejandro
Rensing, Christoph
author_facet Erdt, Mojisola
Fernández, Alejandro
Rensing, Christoph
author_sort Erdt, Mojisola
title Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
title_short Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
title_full Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
title_fullStr Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
title_full_unstemmed Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
title_sort evaluating recommender systems for technology enhanced learning: a quantitative survey
publishDate 2015
url http://sedici.unlp.edu.ar/handle/10915/86675
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AT rensingchristoph evaluatingrecommendersystemsfortechnologyenhancedlearningaquantitativesurvey
bdutipo_str Repositorios
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