An exploration of instructors' and students' perspectives on remote delivery of courses during the COVID‐19 pandemic
Bibliographic record
Abstract
Abstract The world‐wide pivot to remote learning due to the exogenous shocks of COVID‐19 across educational institutions has presented unique challenges and opportunities. This study documents the lived experiences of instructors and students and recommends emerging pathways for teaching and learning strategies post‐pandemic. Seventy‐one instructors and 122 students completed online surveys containing closed and open‐ended questions. Quantitative and qualitative analyses were conducted, including frequencies, chi‐square tests, Welch Two‐Samples t ‐tests, and thematic analyses. The results demonstrated that with effective online tools, remote learning could replicate key components of content delivery, activities, assessments, and virtual proctored exams. However, instructors and students did not want in‐person learning to disappear and recommended flexibility by combining learning opportunities in in‐person, online, and asynchronous course deliveries according to personal preferences. The paper concludes with future directions and how the findings influenced our planning for Fall 2021 delivery. The video abstract for this article is available at https://www.youtube.com/watch?v=F48KBg_d8AE . Practitioner notes What is already known about this topic Emergency Remote Teaching (ERT) allowed institutions across the world to continue teaching and learning at all levels of education during the COVID‐19 pandemic. However, this form of delivery, created under conditions of uncertainty, was developed out of an urgency to keep education going rather than maintaining it at the same level. What this paper adds This study comes after ERT, and is situated between ERT and the return to campus, with some social distancing restrictions still active, in a delivery method widely viewed as “remote delivery”. This is a case study of an entire Canadian higher education institution that implemented remote learning for over one full academic year, documenting and examining instructors' and students' experiences and challenges of the remote learning course delivery format. Quantitative and qualitative data were collected to provide a holistic overview of instructors' and students' experiences of delivery method and assessments including the use of face‐tracking proctoring software. Implications for practice and/or policy Compared to ERT, remote delivery was a thoughtful and deliberate way to transform in‐person courses into virtual learning experiences. Instructors and students were able to successfully replicate many features of in‐person learning and assessments experiences in remote delivery of courses by using effective online tools to teach and learn. As a result, instructors and students called for the use of elements of remote delivery to create more flexible learning opportunities by combining in‐person, live streaming, and asynchronous learning options.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".