An Automatic Recommendation Process to Generate Learning Paths Based on Learner Preferences

Abstract

In the last few years, recommendation systems have become very important and nowadays represent a huge opportunity for e-commerce, social networks and especially e-learning. In particular, it is a commercial tool that increases the revenue of companies, it facilitates the life of users by offering suitable products or services to all Internet users. Moreover, the interest of recommender systems in the context of learning, and in particular distance learning, is that they transform the normal learning system into a personalized learning system. In this paper, we present a new learning path recommendation system based on both the coefficients and scores we have assigned to each topic included in a learning path and on the scores assigned by learners to each topic, expressing their level of interest in a specific area. This system processes a set of input data that are composed on the one hand: parameters resulting from the profiles of the learners, these parameters are scores assigned by each user to each topic, and on the other hand, data related to the learning paths, which show the score or a coefficient that we have assigned to each subject included in the learning path. The objective of this work is to have a system that returns results in the form of appropriate recommendations to each learner who wishes to take courses on a given topic. The results obtained will allow us to have an overview of the preferences and characteristics of the learners in order to better guide them in their educational adventure in each learning path. In fact, this demonstrates the effectiveness and importance of our presented methodology.