Recommender Systems in E-Learning Settings

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