Model to promote STEM careers based on Machine learning for secondary education - Peru

Davila J.P.C.; Salazar A.M.G.; Quintana C.A.; Vargas Y. · Proceedings of the 2025 17th International Conference on Education Technology and Computers, ICETC 2025 (2025) · DOI: 10.1109/icetc66579.2025.11387325

Resumen

Despite growing recognition of the importance of STEM (Science, Technology, Engineering, and Mathematics) careers, female participation in these fields remains significantly lower than that of males. In Peru, social, cultural, educational, and personal factors contribute to this disparity. This study developed a web-based solution to promote STEM career interest among female secondary school students through personalized vocational guidance and inspirational content. A predictive vocational test was implemented within a web application to recommend STEM fields tailored to students' interests. Following the intervention, changes in students' perception, interest, and intention toward pursuing STEM careers were assessed using preand post-intervention surveys. Results showed substantial improvements across key indicators: a 38.1% increase in STEM interest (ICI), a 32.0% increase in intention to pursue STEM (ICN), and a 54.9% enhancement in perception of STEM fields (ICP). Additionally, the machine-learning model integrated into the application achieved a predictive accuracy of 93.3%. This high performance supported the delivery of personalized recommendations and underscores the effectiveness of targeted interventions in fostering STEM awareness and motivation among under-represented populations.

Instituciones: Universidad Peruana de Ciencias Aplicadas