Herramientas computacionales aplicadas al análisis de encuestas estudiantiles en formación científica universitaria: percepciones sobre la educación híbrida en Ciencia y Tecnología de los Alimentos

Abstract

The implementation of hybrid education in science and technology degrees poses organizational and methodological challenges that must be addressed with empirical evidence. This article presents a computational analysis of a survey administered to students of the Bachelor's Degree in Food Science and Technology at the National University of Lanus. The objective is to assess the strengths, weaknesses, and implications of the hybrid model on academic performance and student dropout. Exploratory statistical techniques, data visualization, and text mining were used to process the responses. The results show a positive perception of the flexibility provided by online education, but also highlight issues related to limited teacher support and decreased concentration in asynchronous settings. The study demonstrates the value of survey analysis as a diagnostic and planning tool for pedagogical improvement in strategic science and technology programs, and proposes a data-informed improvement agenda.