Special Issue on: "Recommender Systems to Support the Dynamics of Virtual Learning Communities"
Guest Editors: Olga C. Santos and Jesus G. Boticario, aDeNu Research Group, UNED: Universidad Nacional de Educación a Distancia, Spain
Recommender systems are used in commercial sites like Amazon, Last.fm, Netflix and others to recommend relevant products (or more generally, pieces of information such as books, music from movies, etc) to users according to their interest. It is a successful technique in the entertainment domains which is now being applied into e-learning. Recommenders are changing the way people interact with the web-based applications as they help others to find relevant products and complex information among the huge amount of products and unstructured information available in the web, providing a more personalised information access experience.
Traditionally, much of the published research on recommenders has focused on the algorithms that power the recommendation process. However, many research challenges remain, especially when it comes to applications in specific domains, the design of the interfaces and the social implications of recommenders. Research is still needed to analyse needs and expectations from the users to which recommendations are offered. In particular when recommenders are applied in domains other than the ones traditionally covered, a study of the user needs should be carried out.
This special issue aims to gather a collection of high quality contributions that reflect the support that can be provided by recommender systems in virtual learning communities (VLC). It focuses on the e-learning domain and the virtual learning communities that grow around it.
Users interact in web-based applications that provide social spaces such as learning management systems, and produce contributions with shared learning goals in mind. Moreover, mostly these communities use Web 2.0 services that can provide semantic information. This semantic information together with the contextual information provided by the community infrastructure can feed the recommender strategies. The virtual learning communities that emerge store contextual and semantic information from the contributions provided by the users when they participate in the services offered, such as discussions on forums, shared links, entries in blogs, ratings to learning objects, comments to contributions, etc. This information contributed to the virtual community could become relevant to their mates. These contributions increase continually and the community members are unable to process all of them. Recommenders can provide a valuable functionality to the members of virtual learning communities, recommending what pieces of information shared by the diversity of the members' contributions are the more interesting for each member (taking into account his/her profile).
Moreover, there is a need to take into account user-centred design methods in order to direct the technical design as well as the graphical user interface. In this sense, this special issue calls for papers in the community of researchers for recommender systems in the educational domain that analyse the needs and propose solutions from a user-centred point of view. Contributions are expected to answer some of the following questions (among others):
- How recommendations can impact on the participants in VLC?
- What type of activities within the VLC could be improved or made more productive with the usage of recommender systems?
- What types of situations in VLC are meaningful for providing recommendations?
- How can the utility of a recommender system be measured in a VLC?
- What types of recommendations are of special interest for the educational domain?
- How can users be modelled to facilitate the recommendations process in VLC?
- What is the most appropriate design for a tool to manage the recommendations?
Suitable topics include but are not limited to:
- Impact of adaptations in virtual learning communities
- Educational data mining to improve the usability of virtual learning communities
- Recommender systems for educational domain
- Inclusive recommendations in virtual learning communities (i.e. take into account the diversity of the users, including the accessibility preferences)
- User-centred design methods
- User-centred evaluation
- User modelling to support recommender systems for educational domain
- Personalisation and user needs in educational domain
- Design of the user interface for recommenders in virtual learning communities
- Authoring/administration tools to manage/supervise the recommendations process
- Evaluating recommendation interfaces through user studies and other human-computer interaction approaches
- Recommenders to support social networking in virtual learning communities
- Case studies of recommenders in virtual learning communities
- Evaluation of recommender systems in educational scenarios
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper was not originally copyrighted and if it has been completely re-written).
All papers are refereed through a peer review process. A guide for authors, sample copies and other relevant information for submitting papers are available on the Author Guidelines page
Notification of Intent to Submit: as soon as possible
Submission of Title and Extended Abstract deadline: 28 February, 2010 (recommended, but not mandatory)
Full paper submission deadline: 30 March, 2010
Notification of status & acceptance of paper: 30 May, 2010
Second round of contributions: 30 July, 2010
Final version of paper: 15 September, 2010
Publication of the issue: expected in Winter 2011
Editors and Notes
Manuscripts of the full paper should be submitted through EasyChair. Details on the EasyChair management are given in the aDeNu website page for this special issue: http://adenu.ia.uned.es/web/en/projects/rs4vlc.
Notification of intent to submit and submission of the title and extended abstract should be done directly to Olga C. Santos (email@example.com) in the form of a PDF file attached to the e-mail.