Using Q-Methodology to Evaluate Student Perceptions of Online Anatomy in the Time of COVID-19
Bibliographic record
Abstract
Pursuant to pedagogical changes necessitated by the COVID-19 pandemic, this study was designed to determine which aspects of an online anatomy course students most preferred and most disliked using Q-methodology. Data were collected in fall 2020 and winter 2021, and 166 student responses were analyzed via by-person factor analysis. Three distinct subgroups were identified: Group 1 (n=66) reported being comfortable with the technology skills required for studying anatomy online; Group 2 (n=50) reported dissatisfaction with several elements of course delivery, including evaluations, laboratory assignments, and the amount of lecture content, believing that they were essentially “teaching [themselves]”; Group 3 (n=29) was characterized by being happy with tutorial activities and the guidance received from teaching assistants. Common to all groups was the preference for physical rather than virtual specimens and for faculty-made practice questions as opposed to the overwhelming number of online specimens available for review. There was an overall positive attitude shift among students regarding online delivery across semesters. Given ongoing uncertainty surrounding the pandemic, these findings provide important considerations for future potential online/blended classes on anatomy education.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".