Did the Rapid Transition to Online Learning in Response to COVID-19 Impact Students’ Cognitive Load and Performance in Veterinary Anatomy?
Bibliographic record
Abstract
COVID-19 safety required rapid transitions to online learning across education. This posed unique challenges for veterinary anatomy, which is a practical subject. This study compares the cognitive load and academic performance of first- and second-year veterinary students studying anatomy in 2019 (pre-COVID-19) and 2020 (post-COVID-19 teaching adjustments). Importantly, the core teaching content remained identical for both courses in 2019 and 2020 apart from teaching method (in-person vs. online), allowing us to isolate the effects of teaching method on cognitive load and academic performance. Cognitive load was measured among first- ( n2019 = 105, n2020 = 49) and second-year students ( n2019 = 85, n2020 = 42) at the end of each teaching semester, using a validated instrument. The instrument measures intrinsic load (IL, study material complexity), extraneous load (EL, presentation of material), and germane load (GL, self-perceived learning). t-Tests compared the 2019 and 2020 cohorts with respect to both cognitive load and academic performance. The results indicated that 2019 and 2020 cohorts did not differ on IL or EL in either the first- or second-year subject. However, among both first- and second-year students, the 2020 cohort reported significantly less GL compared to the 2019 cohort. Additionally, the first-year 2020 cohort performed at a significantly lower level than the first-year 2019 cohort. No significant difference in performances was reported between second-year cohorts. Therefore, despite being less inclined to perceive that online course activities enhanced their understanding of anatomy, second-year students with previous experience of learning anatomy in an in-person tertiary environment adjusted better than first-year students with limited experience.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".