Recommender Systems in E-Learning Settings
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (15 September 2011) | Viewed by 11875
Special Issue Editor
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 Issues, Collections and Topics in MDPI journals
Interests: virtual learning environments; game-based instructional tools; user-adaptive technology-enhanced learning; personalized recommendation techniques; case-based reasoning and case-based teaching
Special Issues, Collections and Topics in MDPI journals
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):
Prof. Dr. Mercedes Gómez-Albarrán
Guest Editor
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
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
