INCLUSIÓN DIGITAL Y DEMOCRATIZACIÓN DEL APRENDIZAJE AUTOMÁTICO:
UN ESTUDIO BIBLIOMÉTRICO SOBRE HERRAMIENTAS LOW-CODE/NO-CODE EN LA FORMACIÓN DE CIUDADANOS DESARROLLADORES
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https://doi.org/10.56579/rei.v7i4.1572Palabras clave:
Low-code, No-code, Análisis bibliométrico, Aprendizaje automático, Ciudadanos desarrolladoresResumen
En los últimos años, las herramientas low/no-code han ganado protagonismo como respuesta a la escasez de profesionales calificados ante la creciente demanda de soluciones digitales innovadoras. Este estudio realiza un análisis bibliométrico para investigar cómo estas herramientas, aplicadas al aprendizaje automático, se han integrado en iniciativas de formación de ciudadanos desarrolladores. Este enfoque permite identificar patrones, tendencias y vacíos en la literatura, ofreciendo una visión estructurada de la evolución temática del campo. Se analizaron 351 documentos de la base de datos Scopus, publicados entre 2014 y 2024, mediante cuatro técnicas bibliométricas: (1) análisis de desempeño para medir indicadores cuantitativos; (2) coocurrencia de palabras clave para mapear conceptos centrales; (3) co-citación para identificar la base teórica del campo; y (4) acoplamiento bibliográfico para evidenciar frentes de investigación emergentes. Los resultados indican un crecimiento en la producción científica, con un punto culminante en 2023, lo que señala un fortalecimiento del interés académico en el uso de herramientas low/no-code como estrategia para democratizar el aprendizaje automático. El análisis refuerza la importancia de estas plataformas en la transformación digital y en la inclusión de personas no técnicas en el desarrollo de soluciones avanzadas.
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