Graduation Mentoring Recommender: um modelo de sistema de recomendação de atividades complementares para capacitação profissional do aluno de graduação

Abstract

The search for a personalized education for students has been the subject of study for many years. Through the use of digital platforms, educational institutions have facilities to offer support so that the educational path of students is flexible, with greater focus on areas of interest. As a contribution to a personalized learning process, this paper proposes a recommendation system model to recommend undergraduate students complementary activities according to their professional and personal goals, which complement or extend their current educational path, bringing the student closer to professional areas and complementary activities, requirements that are contained in the MEC guidelines. 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 (FC) techniques, where the objective was to generate recommendations to the student based on the student's access history. In the second experiment, the technique used was Content Based (BC) which the goal to find similar activities based on the contents of the activities. The third experiment was composed by the techniques of the previous experiments, FC and BC, composing a hybrid approach of recommendation. The last experiment was composed by the Graph Based (BG) technique. The development of this work will have as main contributions: to evaluate the benefits that Recommendation Systems, composed with multiple techniques, can offer to the student's formative path; to propose a model of recommendation system for the expansion of the formative path of the undergraduate student through complementary activities according to their professional preferences.