No abstract

In education, the needs of learners are different in the majority of the time, as each has specificities in terms of preferences, performance and goals. Recommendation systems have proven to be an effective way to ensure this learning personalization. Already used and tested in other areas such as e-commerce, their adaptation to the educational context has led to several research studies that have tried to find the best approaches with the best expected results. This article suggests that a hybridization of recommendation systems filtering methods can improve the quality of recommendations. An experiment was conducted to test an approach that combines content-based filtering and collaborative filtering. The results proved to be convincing.

No abstract

The e-learning maintains specific features, as long as it is based on the use of new technologies. They can bring a lot of automation in the evaluation, and consequently help tutors in taking decisions concerning the levels of learners. The reporting tools, in many existing e-learning platforms can then be applied effectively, to bring out all the explicit and implicit aspects which fall within the learner's behavior during the test, and must be taken into account to better adapt his or her level. These aspects can be expressed as parameters which, for a treatment implemented, will lead to a level reassessment algorithm, which will accompany the tutor in taking decisions.