Recommender Systems for an Enhanced Mobile e-Learning

In the last years we have been witnesses of the increasing use of on-line educational systems known as e-learning. Every year there are more teaching centers, both public and private ones, which provide their students with web-based access to Learning Management Systems (LMS). Also, it is important to mention platforms for Massive Open Online Courses (MOOC) which are a type of on-line educational system recently developed according to the design and participation akin to the presential courses at most prestigious universities. These systems provide to all kind of students with didactic resources as well as activities. In another hand the Adaptive and Intelligence Web-based Educational Systems (AIWBES) are made in order to solve the problem of to automate the adaptation of the system to the user features and needs. One more recent alternative is implementing Recommender Systems, which might offer their users customized suggestions to find activities and educational content. Such systems filter user information, for instance, preferences known by a user community for forecasting preferences for the new user, this approach is known as collaborative Filtering. With this proposal we are trying to model and represent the domain knowledge about the learner and learning resources discovering the learners' learning patterns.