Congraduations: sistema de recomendação acadêmica para alunos de TADS do IFAM-CMC usando processamento de linguagem natural

Abstract

This paper addresses the importance of building a solid academic track record during undergraduate studies, highlighting its role in the professional and personal development of university students. The research focuses on the context of the Technology in Systems Analysis and Development (TADS) course at the Federal Institute of Education, Science, and Technology of Amazonas Campus Manaus Centro (IFAM-CMC), where the difficulty of accessing organized information about events, conferences, and graduate programs was identified as a significant barrier to student engagement. The overall objective of the research is to develop a recommendation system based on Natural Language Processing (NLP) to encourage student participation in academic activities. The methodology employed consists of using NLP techniques to analyze descriptions of academic opportunities and thereby generate personalized recommendations that align with students' interests. The expected results include the creation of a functional prototype capable of connecting students to relevant events and programs through newsletters, facilitating their training and professional development. It is concluded that the implementation of such a system can mitigate the lack of clear information, promoting greater engagement and enhancing the academic trajectory of students in the TADS course.