The program seeks to train leaders who positively impact the community. That is why the main characteristics of students interested in being part of the Leaders of Tomorrow's program are [1]: being a talented young Mexican or Central American, having a socioeconomic level that requires 100% financial support to study for a university degree, obtaining a minimum score of 1,300 on the College Board's Academic Aptitude Test (or 70 on the Online Academic Aptitude Test), having participated in a human development project, and having leadership potential. Furthermore, it is a program with low student dropout [1], with only 81 dropouts out of 1,800 young people who have been Leaders of Tomorrow between August 2014 and June 2023 (retention of 95.5%).
Therefore, stakeholders in improving retention strategies and accentuating the social impact of the program and the young people belonging to it are motivated to propose new multidisciplinary approaches based on a curated dataset from Tecnologico de Monterrey and Statistics and Machine Learning models that allow analyzing the admission and permanence profiles of university students developing a social impact project during their studies. Considering the United Nations' Sustainable Development Goals (SDG), the data analysis should contribute to the reflection on SDG 4: Quality Education [2] and the scientific community. Some of the research efforts carried out are focused on analyzing the impact of personal, academic, and emotional-motivational variables on adaptation to Higher Education and university dropout [3-5], student engagement as a predictor of performance, dropout, and well-being in university students [6,7], historical admission data to determine the causes of permanence (retention) in university women [8], and predictive factors of academic success in higher education students, linking factors of learning opportunities, resources, and daily or physical activities [9-12].
Within the expected research proposals, the following approaches are suggested, but not limited to: