CodERS: A hybrid recommender system for an E-learning system

For years, E-learning systems are around to provide students and learners with virtual educational environments in which they have no need to others' assistance in the process of learning. From technical point of view, the major goal of such systems is to improve education and learning levels of users through leveraging the system's facilities. In this regard, it is essential for the system to be able to analyze users' functions and behaviors to prepare them with most appropriate learning materials. In other words, the core component of a working and efficient e-learning system is its recommender system. Since requirements and processes of any recommender system strongly depends on the context, users' behaviors and case-specific goals, in most of situations they should be designed exclusively. Due to this fact, in this paper we present a hybrid and context-specific recommender system (CodERS) for our interactive programming e-learning system, CodeLearnr, and provide an overview on its features including conceptual architecture, workflow and sample outputs.