An Exploratory Analysis on Gender-Related Dropout Students in Distance Learning Higher Education using Machine Learning

Context: School dropout in distance learning has become a growing concern in higher education. Private institutions exhibit a 33.6% dropout rate, while public institutions show a slightly lower rate at 31.2%, with an upward trend. Problem: Studies focus on categorical indicators of lack of time, students' personal lives, the educational institution, and course instructors. However, research is still needed to explicitly focus on identifying patterns related to gender with students abandoning courses. Solution: Identifying gender-related patterns among indicators leading to dropout in 36 distance learning undergraduate courses. Theory: Our study incorporated Tinto theory into how academic performance metrics influence student dropout rates. Vygotsky theory was also instrumental in examining the relationship between students' personal factors, including gender and marital status, and their learning behaviors. Method:The research conducted is descriptive with a quantitative approach. An experiment was carried out to categorizes and identifies the most relevant features influencing dropout using machine learning. Results: The results provide patterns for investigated aspects, highlighting women in most analyses. Time-related characteristics exhibit a higher correlation with dropout. Features related to student academic performance and university campus location play a crucial role in classifying a student as a potential dropout, according to the XGBoost classifier, yielding the best performance results. Conclusion: These analyses offer an understanding of factors influencing distance learning dropout, drawing parallels with gender-related situations influencing dropout decisions. This allows for adopting preventive and personalized measures to enhance student retention and improve the academic experience.