Technical and Practical Implications of Generative Adversarial Networks for Open Science in Education
Supporting Agencies
- Ministerio de Ciencia, Innovación y Universidades
- Departamento de Didáctica e Investigación Educativa
- Universidad de La Laguna
- EDULLAB: Laboratorio de Educación y Nuevas Tecnologías
- Escuela de Doctorado y Estudios de Postgrado
- Gabinete de Planificación y Comunicación
- Vicerrectorado de Agenda Digital, Modernización y Campus Central
- Vicerrectorado de Innovación Docente, Calidad y Campus Anchieta
- Unidad de Docencia Virtual (UDV)
Abstract
Generative Adversarial Networks (GANs), which are characteristic of Artificial Intelligence, allow the creation of synthetic anonymised data useful for Open Science in educational research. This study experiments with the creation of artificial data from a dataset obtained from a survey on levels of use of digital tools and frequency of personal activities with technology. The original data belong to a sample of students from postgraduate degrees at the University of La Laguna. The results show an adequate degree of similarity between the original data set and the set artificially created through predictive algorithms. Obtaining synthetic datasets equivalent to the original ones in structure, shape and extension allows the release of the data to the academic community, safeguarding the protection of confidential information and contrasting a technique that allows the promotion of Open Science from the collection and processing of the data. Generative Adversarial Networks can be used in educational research for the purpose of transparency in methodological and technical procedures and the dissemination of datasets for academic, research and educational purposes.
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