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, 2021
Affiliation
University of Chester; University of Aveiro; Lucian Blaga University of Sibiu; North Carolina State University; SATC College, Criciuma
Metadata
Show full item recordAbstract
This data set is a follow on study from a study on remote learning conducted in 2020 during the first year of the COVID-19 pandemic. It contains data collected from 5 universities in 5 countries about the effectiveness of e-learning during the COVID-19 pandemic in 2021, specifically tailored to mechanical and industrial engineering students. A survey was administered in August 2021 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 the evolution of the student preferences and e-learning effectiveness during the COVID-19 pandemic from 2020 to 2021 by comparing with the dataset from 2020.Citation
Behera, A. K., Alves de Sousa, R., Oleksik, V., Dong, J., & Fritzen, D. (2022). 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, 2021. [Data Set]. UK Data Service. 855604. http://doi.org/10.5255/UKDA-SN-855604Publisher
UK Data ServiceAdditional Links
https://beta.ukdataservice.ac.uk/datacatalogue/studies/study?id=855604https://reshare.ukdataservice.ac.uk/855604/
Type
DatasetCollections
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International