Automatic Recommendation Approach for Adaptive E Learning Systems

In this work we exploit recommendation systems to propose an approach capable of providing recommendations regarding appropriate learning paths in adaptive E Learning systems. The recommended learning paths best satisfy learners while meeting their needs and preferences in particular, their learning levels relating to the different courses in different subjects made available to them on the E Learning platforms. The proposed approach is based on processing data from learners’ interactions with different courses of different depth levels and then returning the processing results in the form of learning path recommendations taking into account the levels of each learner. The recommendation algorithm proposed in this work will be described and implemented and simulation tests will also be presented to show the performance of the proposed approach.