Interaction Identified as both a Challenge and a Benefit in a Rapid Switch to Online Teaching during the COVID-19 Pandemic
Bibliographic record
Abstract
The recent emergence and subsequent global spread of COVID-19 has forced a rapid shift to online and remote learning at veterinary schools. Students in a Bachelor of Veterinary Medicine program were taught using a real-time online platform for one semester, with recorded synchronous lectures and tutorials, virtual laboratories, and clinical skills classes where possible. Students in all years of the program were surveyed twice, 8 weeks apart to assess their perceptions of online teaching and to identify challenges they experienced. Using a 10-point Likert scale, students agreed that they could achieve their learning outcomes using online learning with no more difficulty than with face-to-face teaching, allocating average scores of 7.6 and 8.2 at each time point. Students were overwhelmingly positive about the impact of online teaching on time-management of their learning due to the loss of travel time. They enjoyed aspects of teaching such as recorded lectures, online polls quizzes, and chat boxes that allowed more student-focused learning. However, there were concerns about the reduction in face-to-face interactions including loss of classroom atmosphere and reduced interaction with peers. Students experienced technical problems in a median of 20% of lectures (range 10%-50%) at the first survey and 10% at the second (range 10%-50%). Increased use of strategies to optimize peer interactions is recommended to facilitate student learning using online platforms. Moving forward beyond the pandemic, allowing flexible time management and a shift toward student-centered learning using strategies such as flipped classrooms may be beneficial.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".