Graduation Mentoring Recommender - Hybrid Recommendation System for Customizing the Undergraduate Student’s Formative Path

The search for a personalized education for students has been the subject of study for many years. As a contribution to a personalized learning process, this paper proposes a recommendation system model to suggest complementary activities according to their professional and personal goals. As part of the construction of the model, two experiments were conducted to better understand the scenario of the recommendations and obtain information. The first experiment used Collaborative Filtering (CF) techniques, where the objective was to generate a list of recommendations to the student based on the activities already taken. The second expeliment, besides using CF techniques, was based on the content of the activities, composing a hybrid approach to recommendation. The main contributions of this work are to evaluate the benefits that recommendation systems can offer to the student’s formative path and to propose a model of recommendation system for the expansion of the formative path of the undergladuate student through complementary activities according to their professional preferences.