Using Q‐Methodology to Evaluate Student Perceptions of Online Anatomy in the Time of COVID‐19
Bibliographic record
Abstract
Background Since the seventeenth century, the primary approach to teaching anatomy has involved hands‐on learning using cadaveric specimens. However, the ability to use this long‐standing tradition was curtailed in the 2020‐2021 school year due to the COVID‐19 pandemic. Many institutions closed physical classrooms entirely, launching experiential courses, such as anatomy, into the online space. Hypothesis We hypothesized that Q‐methodology could be used to uncover student perceptions of an introductory anatomy and physiology course that was offered online for the very first time. Methods Q‐methodology, considered the study of subjectivity, is an approach that statistically uncovers groups of individuals with shared perceptions within a larger cohort. Instructors can use Q‐methodology to identify groups of students with shared needs, allowing for more specific and productive course reform. In the current study, Q‐methodology was used as a means of course evaluation in the fall 2020 and winter 2021 semesters. Students were asked to sort 44 opinion‐based statements in a quasi‐normal table based on their level of agreement. By‐person factor analysis of 166 responses revealed three statistically distinct groups of students. Results The three groups were assigned the following monikers: Connected and Contented (CC), Disconnected and Disgruntled (DD), and Interconnected and Collaborative (IC). CC students (n=66) felt generally ambivalent toward course components and were comfortable with the technology skills required to participate in the online course space. DD students (n=50) were deeply unhappy with several elements of the course, including lectures, assignments, and evaluations. These students also felt as if they were teaching themselves. Finally, IC students (n=29) looked favourably upon the tutorial space and the role of teaching assistants. Analysis also revealed that some sentiments were shared across all three groups, including the preference for physical rather than virtual specimens, and the desire for more practice questions from faculty in order to prepare for bellringer exams. Interestingly, cohort opinions did not remain static across both semesters. There was a positive attitude shift as more students felt “Disconnected and Disgruntled” in the fall, and “Connected and Contented” in the winter. Conclusions These findings are useful for anatomy instructors interested in transitioning courses to an online or blended space, particularly in the face of ever evolving public health restrictions. The current study also models the wealth of information that can be uncovered using Q‐methodology ‐ useful for anyone interested in the previously amorphous study of subjectivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".