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Abstract

The constant demands that technology creates in aerospace engineering also influence education. The identification of the technologies with practical application in aerospace engineering is of current interest to decision makers in both universities and industry. A social network approach enhances this scoping review of the research literature to identify the main topics using the Big Five technologies in aerospace engineering education. The conceptual structure of the dataset (n=447) was analyzed from different approaches: at macro-level, a comparative of the digital technology identified by cluster analysis with the number of co-words established in 3 and 8 and, a keyword central structure (n=8) at micro-level. The articles were categorized by the type of digital technology and, those related to the educational context (n=86) were co-word analyzed to study the relationships between basic and applied research. A total of 18 selected studies were analyzed from a design-based research approach. Findings reveal that Big Data, IoT (2002-2008) and, cloud computing (2010-) were initially applied in the aerospace engineering field. Only Cloud computing (2012) and, Big Data (2017) were transferred towards more educational research. Cloud computer appears related to collaborative work and classroom education. Big data is related to computer-aided design in engineering education. Only Web 2.0 (n=3) is used in the teaching of aeronautical engineering, without any interaction identified in the basic research. Most of the selected studies addressed the undergraduates students and the instructional approach strategy with the result of the potential for improved student learning.

Acknowledgements

This work was supported by the Ministry of Education and Science of the Russian Federation and the Russian Academic Excellence Project '5-100'. Many thanks to Professor Valeriy Dmitrievich Elenev, Director of the Institute of Aviation Technology at Samara National Research University (Russia), special thankfulness to professors and researchers of the Institute for their support to this study. Thanks to Pilar Gálvez Corral, a student of the master’s degree in the field of ICT for language education and processing of the UNED (National University of Distance Education. Spain), for her collaboration in the classification of the dataset in the Big Five technologies.

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