Enrollment Recommendation System based on Student Profile and Progress

In the last decades, many technological advances have been achieved and, currently, many with the application of Artificial Intelligence, which is increasingly being used in all sectors of society and even in higher education institutions. Recommender systems have been used in a wide spectrum of applications. Focused on the community and on the academic processes performed daily, this work aims to help undergraduate students at the time of enrollment through recommendation systems, which direct the choice of disciplines to those that best suit the student's academic moment. The article presents the proposed course recommender and uses a Computer Science course as a case study. As a result, recommendations were generated for the first half of 2019 and compared with the requested, enrolled and approved subjects. After carrying out the tests and comparing with what was requested and attended by the student, more than 60%.