Applying neural networks in student satisfaction analysis: Implications for university management

This study analyzes student satisfaction at a university using a structured survey and advanced artificial intelligence techniques, specifically neural networks. The main objective is to identify the key factors in students’ perception of educational quality. The methodology involved a survey with 38 items on a Likert scale of 1 to 7, applied to a diverse sample of undergraduate, postgraduate, and exchange students during the years 2022 and 2023. The final sample consisted of 9623 valid records. Artificial intelligence techniques were employed to analyze the data, with neural networks trained under the supervised learning paradigm to predict levels of student satisfaction. The results show a high correlation between satisfaction with the cashier service and overall student satisfaction, highlighting the importance of administrative services. Additionally, a close relationship was identified between the institutional mission and the educational process, suggesting that a clear and accessible mission improves student perception. The effectiveness of neural networks was demonstrated, achieving high precision and sensitivity in their predictions. In conclusion, this study provides valuable insights into the factors influencing student satisfaction and demonstrates the potential of artificial intelligence techniques to improve educational management. The findings offer a solid foundation for future research and practical improvements in higher education.