  This study was based on data analysis of academic histories of civil
engineering students at FACET-UNT. Our main objective was to determine the
academic performance variables that have a significant impact on the dropout of
the career. To do this, we implemented a correlation model using LightGBM
(Barbier et al., 2016; Ke et al., 2017; Shi et al., 2022). We use this model to
identify the key variables that influence the probability of student dropout.
  In addition, we use game theory to interpret the results obtained.
Specifically, we use the SHAP library (Lundberg et al., 2018, 2020; Lundberg &
Lee, 2017) in Python to calculate the Shapley numbers.
  The results of our study revealed the most important variables that influence
the dropout from the civil engineering career. Significant differences were
identified in terms of age, time spent in studies, and academic performance,
which includes the number of courses passed and the number of exams taken.
These results may be useful to develop more effective student retention
strategies and improve academic success in this discipline.
