Incorporating MOOC and COVID-19-Related Scientific Papers into Veterinary Microbiology Teaching to Enhance Students’ Learning Performance and Professional Recognition
Bibliographic record
Abstract
The COVID-19 pandemic has exerted a huge adverse influence on global teaching activities and students’ psychological status. Veterinary microbiology is mainly concerned with bacterial and viral diseases, including coronavirus diseases. An innovative online-to-offline teaching approach for this course was established to stimulate students’ learning initiative and mitigate their anxiety about COVID-19. A well-established massive open online course (MOOC) was first adopted as preview material before class, followed by in-person teaching. Additionally, COVID-19-related scientific papers were used as pre-class reading material in veterinary microbiology and were further explained in class. The effect of this innovative teaching mode was systematically evaluated by final examination scores and questionnaires. The average score (81.75) and excellence score rating (> 85 scores, 37.3%) resulting from this blended teaching mode were not statistically higher than those of the online-only (79.19, p = .115; 28.6%, p = .317) or offline-only (79.47, p = .151; 27.9%, p = .269) teaching modes. This may be due to the sample size investigated; however, the results indicate that the innovative teaching mode did not decrease teaching quality. Additionally, most subjects (72.9%) were satisfied with the blended mode and supported its future use. Intriguingly, the introduction of COVID-19-related scientific papers helped students understand virology, relieve their anxiety, and increase their professional identity. Collectively, the innovative approach to teaching veterinary microbiology in this study provides a beneficial reference for other teachers to maintain and improve teaching quality.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".