Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic




Data analysis, CAD, COVID-19, Online learning, Statistical survey, Engineering education


The COVID-19 pandemic has challenged many educational institutions around the world in 2020 and 2021 as traditional education has been interrupted to prevent the spread of the virus. This forced the transition from traditional education to fully distance learning envi-ronments for all levels of education. The widespread adoption of distance learning has led instructors to form new digital learning environments and methods. In response to this unexpected situation, data regarding engineering students and their interaction with the learning environment was accumulated and processed, generating a matrix of 129 × 165 variables. The motivation for this research is to identify new variables that impact student performance during the disorientation of the educational process due to the COVID-19 pandemic. Statistical analysis was performed and discussed in this paper including correla-tion analysis, factor analysis, and clustering. Reliability analysis was also performed and ANOVA (analysis of variance) was applied to clusters. The novelty of this work is to use student performance data and statistical analysis of online surveys to reveal patterns that can help reduce dropout rates and transform the educational process, under extenuating and imposed distance learning circumstances. A major finding is that by applying innovative teaching methods, thereby meeting the challenge of an imposed distance learning environ-ment, students' spatial conceptions improve, overcoming the absence of a physical learning space. Deep insights for individual students were discovered, as well as significant relation-ships between students' transition from secondary to higher education and their understand-ing of geometric features. Evidence of the effectiveness of the online learning framework that was integrated showed that it positively influenced students' learning styles.

Author Biographies

Zoe Kanetaki, University of West Attica Athens, Greece

Zoe Kanetaki is a Lecturer in the Department of Mechanical Engineering, University of West Attica. She received her degree in Architecture from E.S.A. and her M.Sc. degree in Urbanism and Regional Planning from NTUA. Her re-search interests include online learning, data analysis, engineering education and CAD.

Constantinos Stergiou, University of West Attica Athens, Greece

Constantinos Stergiou is Professor and Head of the Mechanical Engineering Department at the University of West Attica. He received his degree in Mechanical Engineering from NTUA, Greece and his Ph.D. from Technische Universität Darmstadt. His research interests lie in the field of Engineering Design, CAD/CAM/CAE and Additive Manufacturing.

Georgios Bekas, University of West Attica Athens, Greece

Georgios Bekas holds a PhD in Civil and Structural Engineering. His research interests include Operations Research, Machine Learning, and optimization of Civil and Energy Engineering works.

Christos Troussas, University of West Attica Athens, Greece

Christos Troussas is a Post-doctoral Researcher in the Department of Informatics and Computer Engineering, University of West Attica, Greece. He received the B.Sc., M.Sc., and Ph.D. degrees in Informatics from the Department of Informatics, University of Piraeus, Greece. His current research interests include software engineering, multiagent systems, adaptive HCI, and artificial intelligence.

Cleo Sgouropoulou, University of West Attica Athens, Greece

Cleo Sgouropoulou is Vice Rector of the University of West Attica, Greece and Professor in the Department of Informatics and Computer Engineering of the same University. She received the B.Sc. and Ph.D. degrees s in electrical and computer engineering from the Department of Electrical and Computer Engineering, NTUA, Greece. Her research interests include artificial intelligence in education and software engineering.




How to Cite

Kanetaki, Z., Stergiou, C., Bekas, G., Troussas, C., & Sgouropoulou, C. (2021). Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic. International Journal of Engineering Pedagogy (iJEP), 11(6), pp. 27–49.