Educación, Big Data e Inteligencia Artificial: Metodologías mixtas en plataformas digitales

Education, Big Data and Artificial Intelligence: Mixed methods in digital platforms

 Beatrice Bonami, São Paulo (Brasil)

 Luiz Piazentini, Londres (Reino Unido)

 André Dala-Possa, Santa Catarina (Brasil)

https://doi.org/10.3916/C65-2020-04

Palabras clave

Inteligencia artificial, macrodatos, educación, metodologías mixtas, multimodalidad, tecnología digital, sociedad de las pantallas, conexión digital

Resumen

La tecnología digital ha traído características de conexión que restablecen nuestra comprensión de arquitecturas sociales. Sobre la Inteligencia Artificial (IA) y Big Data, el campo educativo reorganiza su estructura para considerar a los actores humanos y no humanos y sus acciones en plataformas digitales. En este escenario cada vez más complejo, esta propuesta tiene como objetivo presentar definiciones y debates sobre IA y Big Data de naturaleza académica o publicados por organizaciones internacionales. El estudio de IA y Big Data puede ir más allá de la búsqueda de poder computacional / lógico y entrar en áreas menos difíciles (y quizás más complejas) del campo científico para responder a sus impactos sociales en la educación. Esta investigación sugiere un análisis de la educación a través de las habilidades del siglo XXI y los impactos del desarrollo de IA en la era de las plataformas, pasando por tres ejes de grupos metodológicos: investigación, aplicación y evaluación. Para llevar a cabo la investigación, confiamos en revisiones sistemáticas, investigaciones bibliográficas y análisis de calidad de estudios de casos para componer un documento de posición que arroje luz sobre cómo funcionan la IA y el Big Data y en qué nivel se pueden aplicar en el campo de la educación. Nuestro objetivo es ofrecer un análisis triangular bajo un enfoque multimodal para comprender mejor la interfaz entre la educación y las nuevas perspectivas tecnológicas.

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Ficha técnica

Recibido: 27-02-2020

Revisado: 19-03-2020

Aceptado: 27-04-2020

OnlineFirst: 15-06-2020

Fecha publicación: 01-10-2020

Tiempo de revisión del artículo : 21 (en días) | Media de tiempo de revisión de los manuscritos del número 65: 36 (en días)

Tiempo de aceptación del artículo: 59 (en días) | Media tiempo aceptación de los manuscritos del número 65: 78 (en días)

Tiempo de edición OnlineFirst: 171 (en días) | Media tiempo edición de los OnlineFirst del número 65: 190 (en días)

Tiempo de publicacicón final del artículo: 216 (en días) | Media tiempo de publicación final de los articulos del número 65: 235 (en días)

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