Multi-Agent System for Recommending Learning Objects in E-Learning Environments

This full paper of innovate-to-practice category presents a Multiagent System for recommending learning objects in Virtual Learning Environments (VLE), aiming to improve the customization of instructional guidance on educational content according to the student's profile. The methodology was initially research aimed at identifying the motivators of the students' performance and their weaknesses, adopting a personalized student model based on the level of knowledge, and providing predictive models to monitor the student's progress in the curriculum. This framework provides a distributed architecture, and consists of three layers: 1) Administrative layer; 2) Storage layer; 3) Pedagogical layer. For the recommendation of learning objects, a collaborative filter was used, which constitutes a successful technique in several recommendation applications, seeking similarities in users' habits to predict their future decisions.