Enhancing Vocational Guidance with Machine Learning: Predicting STEM Career Viability for High School Students

Melissa Moreno-Novoa, Edwin Puertas, Juan Carlos Martinez-Santos

Abstract: The decision to pursue a professional career is paramount for young people. Yet, the absence of adequate guidance can result in misguided choices. In Colombia, most high school students need a clearer understanding of their future career aspirations. This lack of clarity often leads to a significant dropout rate at the university level. Furthermore, students entering university tend to consider something other than STEM careers, which could result in a mismatch between their labor skills and future economic needs. To address this issue, we apply machine learning, data mining, and utilizing information from tests such as Saber 11 and the Gardner test, which aim to predict viability within their capabilities oriented toward STEM careers. Applying machine learning models such as XGBoost, Stacking, Random Forest, Decision Tree, and KNN has shown promising results, albeit with challenges in balancing precision and recall for different classes. These advancements represent an opportunity to enhance vocational guidance and increase the likelihood of success and job satisfaction among young people.