Transformation of Robotics Education in the Era of Covid-19: Challenges and Opportunities.

Blended Learning COVID-19 Higher Education Robotics STEM Virtual Reality

Journal

IFAC-PapersOnLine
ISSN: 2405-8963
Titre abrégé: IFAC Pap OnLine
Pays: Netherlands
ID NLM: 9918769088906676

Informations de publication

Date de publication:
2022
Historique:
medline: 1 1 2022
pubmed: 1 1 2022
entrez: 15 4 2024
Statut: ppublish

Résumé

The COVID-19 pandemic has significantly impacted many aspects of our social and professional life. To this end, Higher Education institutions reacted rather vastly to this unpreceded situation although many issues have been reported in the international literature since the emergence of the first global lockdown. As we are now transitioning back to the 'normality', universities and businesses consider the so-called 'blended' or 'hybrid' model as a means of facilitating the transition phase. In view of this decision, several studies can be identified wherein blended learning scenarios are proposed and described. The present work constitutes such an effort. Precisely, while adjusting the lens to the didactic of Robotics courses, we propose a blended learning model via which the laboratory activities are performed without the physical presence of the students in the physical context. The aforementioned objective is attained under the aid of the Virtual Reality technology coupled with the Digital Twin model. We hope that the ideas presented in this manuscript will motivate and inspire more researchers, instructional designers, and educators to consider the adoption of such alternative instructional techniques to mitigate the shortcomings that the remote education setting brings and further to improve the overall learning experience.

Identifiants

pubmed: 38620933
doi: 10.1016/j.ifacol.2022.10.173
pii: S2405-8963(22)02187-5
pmc: PMC9605726
doi:

Types de publication

Journal Article

Langues

eng

Pagination

2908-2913

Informations de copyright

© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.

Auteurs

Athanasios Christopoulos (A)

Centre for Learning Analytics, University of Turku, Turku, Finland.

Guido Coppo (G)

Synarea, Torino, Italy, 10153.

Salvatore Andolina (S)

Synarea, Torino, Italy, 10153.

Simone Lo Priore (SL)

Synarea, Torino, Italy, 10153.

Dario Antonelli (D)

Dipartimento di Ingegneria Gestionale e della Produzione, Politecnico di Torino, Turin, Italy, 10129.

Dimitrios Salmas (D)

Department of Informatics and Telecommunications, University of Ioannina, Arta, Greece, 47100.

Chrysostomos Stylios (C)

Department of Informatics and Telecommunications, University of Ioannina, Arta, Greece, 47100.
Industrial Systems Institute, Athena RC, Patras Science Park Building, Stadiou Str. GR:26504, Patras, Greece.

Mikko-Jussi Laakso (MJ)

Centre for Learning Analytics, University of Turku, Turku, Finland.

Classifications MeSH