- Published
- 28 December 2021
- Conference item
Teaching through a global pandemic: educational landscapes before, during and after COVID-19
- Authors
- Source
- ITiCSE-WGR '21: Proceedings of the 2021 Working Group Reports on Innovation and Technology in Computer Science Education
Full text
Abstract
The coronavirus (COVID-19) pandemic has forced an unprecedented global shift within higher education in how instructors communicate with and educate students. This necessary paradigm shift has compelled educators to take a critical look at their teaching styles and use of technology. Computing education traditionally focuses on experiential, in-person activities. The pandemic has mandated that educators reconsider their use of student time and has catalysed overnight innovations in the educational setting. Even in the unlikely event that we return entirely to pre-pandemic norms, many new practices have emerged that offer valuable lessons to be carried forward into our post-COVID-19 teaching. This working group will explore what the post-COVID-19 academic landscape might look like, and how we can use lessons learned during this educational shift to improve our subsequent practice. Following a multinational study of computing faculty, this exploratory stage will identify practices within computing that appear to have been improved through exposure to online tools and technologies, and that should therefore continue to be used in the online space. In the broadest sense, our motivation is to explore what the post-COVID-19 educational landscape will look like for computing education.
Cite as
Siegel, A., Zarb, M., Alshaigy, B., Blanchard, J., Crick, T., Glassey, R., Hott, J., Latulipe, C., Riedesel, C., Senapathi, M., Simon & Williams, D. 2021, 'Teaching through a global pandemic: educational landscapes before, during and after COVID-19', ITiCSE-WGR '21: Proceedings of the 2021 Working Group Reports on Innovation and Technology in Computer Science Education, pp. 1-25. https://doi.org/10.1145/3502870.3506565
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- Repository URI
- https://rgu-repository.worktribe.com/output/1575345