Modelagem matemática para recomendação de orientadores em programas de pós-graduação stricto sensu: uma abordagem baseada na análise de métricas acadêmicas e científicas

Abstract

The selection of academic advisors and stricto sensu graduate pro- grams is an increasingly complex and crucial process for students, requiring the analysis of a vast amount of data related to programs, research groups, research lines, as well as factors such as acade- mic compatibility, advisor reputation, scientific production, and collaboration networks. Traditionally, this decision is made manually and often based on sub- jective perceptions and informal advice, which can lead to poorly grounded choices misaligned with students’ academic and professional goals. To sup- port this challenge, this project proposes the development of a mathematical and computational model that integrates data science techniques to calculate a Recommendation Index (Índice de Recomendação (IR)) based on acade- mic indicators extracted from Lattes Platform curricula and other international databases. This model aims to provide objective recommendations, conside- ring criteria such as scientific productivity, mentoring experience, and research area alignment. The model validation was conducted through statistical analy- ses applied to faculty curricula extracted from the Lattes Platform, including data completeness tests, descriptive analysis of metrics, identification of outli- ers, and evaluation of the consistency and robustness of the results. Thus, the proposed approach seeks to foster a more objective, well-founded, and individualized process for selecting academic advisors and graduate programs.