Multi-metric Evaluation of the Effectiveness of Remote Learning in Mechanical and Industrial Engineering During the COVID-19 Pandemic: Indicators and Guidance for Future Preparedness
AffiliationUniversity of Chester; University of Aveiro; Lucian Blaga University of Sibiu; North Carolina State University; SATC College
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AbstractThis data set contains data collected from 5 universities in 5 countries about the effectiveness of e-learning during the COVID-19 pandemic, specifically tailored to mechanical and industrial engineering students. A survey was administered in May, 2020 at these universities simultaneously, using Google Forms. The survey had 41 questions, including 24 questions on a 5-point Likert scale. The survey questions gathered data on their program of study, year of study, university of enrolment and mode of accessing their online learning content. The Likert scale questions on the survey gathered data on the effectiveness of digital delivery tools, student preferences for remote learning and the success of the digital delivery tools during the pandemic. All students enrolled in modules taught by the authors of this study were encouraged to fill the survey up. Additionally, remaining students in the departments associated with the authors were also encouraged to fill up the form through emails sent on mailing lists. The survey was also advertised on external websites such as survey circle and facebook. Crucial insights have been obtained after analysing this data set that link the student demographic profile (gender, program of study, year of study, university) to their preferences for remote learning and effectiveness of digital delivery tools. This data set can be used for further comparative studies and was useful to get a snapshot of student preferences and e-learning effectiveness during the COVID-19 pandemic, which required the use of e-learning tools on a wider scale than previously and using new modes such as video conferencing that were set up within a short timeframe of a few days or weeks.
CitationBehera, A. K., Alves de Sousa, R., Oleksik, V., Dong, J., & Fritzen, D. (2021). Multi-metric evaluation of the effectiveness of remote learning in mechanical and industrial engineering during the COVID-19 pandemic: Indicators and guidance for future preparedness [Data set]. UK Data Service (855089). http://doi.org/10.5255/UKDA-SN-855089
PublisherUK Data Service
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