Recommender system for Web 2.0 supported eLearning

The paper presents the work-in-progress with the aim to develop recommender system for personalization of activities in e-learning 2.0 environment. The main components of the proposed system are activity, student and group models, and recommender module. Activity model will be used for learning design representation and will include items that could be recommended to students: e-tivities, possible collaborators, tools, and advices. To provide recommendations tailored to the student's and group's characteristics, an important component of the system will include student and group models. The emphasis of the research is on the procedures for assessing the student's (group's) activity level based on the data collected from the third party services (Web 2.0 tools). Student's model will also represent knowledge level and preferences. The recommender module, as third component of the system, will include original pedagogical rules together with the algorithms that adapt known recommendations techniques to the educational context.