Open Educational Resources in the Time of COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic caused many post-secondary institutions to close abruptly in early 2020, and instructors were expected to transition to remote online instruction with little notice. For many instructors, hastily recorded lecture-capture videos alongside digital slides became the default mode of sharing instructional content. This sudden shift to video-based instruction was a significant challenge but also presented an opportunity to develop some instructional videos as open educational resources (OER). This paper outlines two case studies from the University of Saskatchewan in which a mix of OER and class-specific, closed-content videos were designed and integrated into remote learning environments. In designing these videos, we focused on technical design elements and accessibility, ability to reuse and share, and student engagement. Both cases, one in veterinary microbiology and the other in music research methods, followed similar strategies for creating multiple types of video content for the course, focusing on four distinct types (labs and demonstrations, guest interviews, lectures, and course information). Choosing to develop and share some of this video content as OER allowed us to expand the use of these learning objects beyond the online classroom. We discuss our considerations for making some videos open, including novelty of the content, reusability, copyright, privacy, and demands on instructor time. We also provide an introduction to our production process and practical tips, including planning, audiovisual production, editing, accessibility, and sharing platforms. The COVID-19 closures made 2020 an unexpectedly challenging year for students and instructors, but the necessity of moving instruction online prompted us to focus on supporting students in this new environment and helped us contribute to the growing body of OER.
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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 | 0.008 | 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 | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".