Design process for the generation of future education scenarios

 Alessandro Manetti, Barcelona (Spain)

 Pablo Lara-Navarra, Barcelona (Spain)

 Jordi Sánchez-Navarro, Barcelona (Spain)

https://doi.org/10.3916/C73-2022-03

Keywords

Education, studies on the future, design, mixed methods, data mining, trends

Abstract

This paper brings together studies on the future and design methodologies to develop a novel proposal for prospective analysis in the field of education. We apply mixed research methods in combination with design methodologies to open up new routes for studying the evolution, impact and behaviour of trends in future scenarios. Our research is based on an analysis of qualitative data from secondary sources and interviews with experts, which we transform into quantitative data using fuzzy logic models applied to uncontrolled Internet data environments. Our goal is to verify the validity of the DEFLEXOR method, which addresses the need to identify educational opportunities based on future scenarios defined using megatrends detected in various fields. Our conclusions highlight that combining qualitative and quantitative approaches with the methodological principles of design thinking, together with automated calculations arising from creative reflection by experts, constitutes a powerful methodology for developing specific prospective studies.

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Fundref

Research Group on Learning, Media and Environment (Spanish acronym GAME) (2017 SGR 293). This research is part of the project titled Una propuesta de herramienta para el análisis y la planificación estratégica de innovación basada en el estudio de macrotendencias y en metodologías de diseño (A Proposed Tool for Analysing and Strategically Planning Innovation Based on the Study of Macrotrends and on Design Methodologies) (2018 DI 031), funded by the University Grants Management Agency of the Government of Catalonia.

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Technical information

Received: 27-02-2022

Revised: 05-04-2022

Accepted: 12-05-2022

OnlineFirst: 30-06-2022

Publication date: 01-10-2022

Article revision time: 37 days | Average time revision issue 73: 44 days

Article acceptance time: 74 days | Average time of acceptance issue 73: 89 days

Preprint editing time: 171 days | Average editing time preprint issue 73: 186 days

Article editing time: 216 days | Average editing time issue 73: 231 days

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