Special Issue "Recommender Systems in E-Learning Settings"

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A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 September 2011)

Special Issue Editor

Guest Editor
Prof. Dr. Mercedes Gómez-Albarrán (Website)

Dep. Ingeniería del Software e Inteligencia Artificial, Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain
Interests: virtual learning environments; game-based instructional tools; user-adaptive technology-enhanced learning; personalized recommendation techniques; case-based reasoning and case-based teaching

Special Issue Information

Dear Colleagues,

In our everyday lives we are exposed to an amount of information that increases far more quickly than our ability to process it. Recommender systems have successfully helped users to alleviate this information overload by supporting them in pre-selecting information they may be interested in.

Recommender systems have been traditionally applied in e-commerce. However, their use has been recently transferred to the learning field. In this sense, we can found in the literature works that follow different approaches and apply to different (e-)learning contexts.

As for learner-centred context, some research has been conducted into developing recommender tools for courses and curriculum learning activities. The massive increase of online learning resources also provides opportunities for the design, development and evaluation of recommender systems that support learners in decision-making and the identification of suitable resources in, both, formal and non-formal settings. Finding other people with relevant learning interests can also be supported by recommender systems.

As for instructor-centred context, recommenders can suggest to the instructors the most appropriate modifications for improving the effectiveness of web-based educational courses. Recommendation of alternative learning paths through learning resources can also support teaching tasks.

This special issue is open to researchers interested in the conception of approaches and the design, development and evaluation of recommender tools in the e-learning field. Papers describing novel recommendation approaches, state-of-the-art recommender tools and/or experience reports about their use are welcome.

Suitable topics (include but are not limited to):

  • Requirements for the deployment of recommender systems in e-learning
  • Relevant recommendation algorithms for e-learning
  • Adaptation and personalization in e-learning recommendations
  • Diversity-enhanced recommendation approaches in educational context
  • Recommendation for individual learner and virtual learning community scenarios
  • Suitable user modelling in educational recommendation systems
  • Design and development aspects and experiences of recommender tools applied to e-learning
  • Learner-centred evaluation methods for recommendation in e-learning
  • Experiences in evaluation of recommender tools in the educational context
  • Case studies for educational recommender systems in real world scenarios

Prof. Dr. Mercedes Gómez-Albarrán
Guest Editor

Keywords

  • educational recommender tools
  • personalized recommendation in e-learning
  • diversity-aware recommendation in e-learning
  • design, development and evaluation of educational recommenders
  • recommendation and learning communities user-modelling for recommender tools applied to e-learning

Published Papers (1 paper)

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Research

Open AccessArticle Requirements for Semantic Educational Recommender Systems in Formal E-Learning Scenarios
Algorithms 2011, 4(2), 131-154; doi:10.3390/a4030131
Received: 4 March 2011 / Revised: 23 May 2011 / Accepted: 1 July 2011 / Published: 20 July 2011
Cited by 19 | PDF Full-text (496 KB) | HTML Full-text | XML Full-text
Abstract
This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take [...] Read more.
This paper analyzes how recommender systems can be applied to current e-learning systems to guide learners in personalized inclusive e-learning scenarios. Recommendations can be used to overcome current limitations of learning management systems in providing personalization and accessibility features. Recommenders can take advantage of standards-based solutions to provide inclusive support. To this end we have identified the need for developing semantic educational recommender systems, which are able to extend existing learning management systems with adaptive navigation support. In this paper we present three requirements to be considered in developing these semantic educational recommender systems, which are in line with the service-oriented approach of the third generation of learning management systems, namely: (i) a recommendation model; (ii) an open standards-based service-oriented architecture; and (iii) a usable and accessible graphical user interface to deliver the recommendations. Full article
(This article belongs to the Special Issue Recommender Systems in E-Learning Settings)
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