Using Q Methodology to Evaluate Online Anatomy Education: Learning in a COVID‐19 Context
Bibliographic record
Abstract
Introduction Anatomy education traditionally relies on in‐person learning and experiential skill development. However, the ongoing COVID‐19 pandemic has forced many courses to adopt online modes of delivery, and anatomy is no exception. The question now is whether anatomy education has successfully made the transition to the digital space, particularly with respect to the perception of learners. Objective The current study seeks to understand how students view an online, introductory anatomy and physiology course. Traditional methods of course evaluation include Likert scales and open‐ended responses; however, such methods fail to consider both the diversity of learners and the need for course‐specific feedback. Thus, we used the emerging area of Q methodology as a tool for course evaluation. Hypothesis We hypothesize that Q methodology will enable us to uncover student opinions which are specifically relevant to their online course experience. Methods Q methodology can be used to identify groups of learners with shared perceptions, allowing educators to better understand and respond to the needs of students. Studies consist of three phases: survey instrument development, data collection, and analysis/interpretation. First, a list of opinion‐based statements regarding anatomy education are selected. Next, students rank statements based on their level of agreement. Finally, rankings undergo a by‐person factor analysis to categorize students into distinct groups with shared perceptions. Results Data was collected from 106 students at McMaster University. Factor analysis revealed three distinct subgroups within the cohort. Group 1 (n = 45) felt they needed more time on their evaluations and lectures did not cover an appropriate amount of content. Group 2 (n = 30) did not enjoy synchronous tutorials or labs. Group 3 (n = 21) overall was satisfied with course delivery. Certain perceptions were also shared among all three groups. There was a consensus among students that they generally disliked online learning compared to in‐person learning, with particular concern surrounding the use of virtual specimens and bellringer exams. Students, however, appreciated the availability of asynchronous lectures as a mode of online content delivery. Age, sex, program, education history, and anticipated grade were not associated with cohort subgroupings. Conclusion Interestingly, results of this study recognize similar course strengths/limitations noted in in‐person classes (e.g., value of in‐person laboratories and assessment concerns reported elsewhere), but also highlight key areas of strength (asynchronous lectures) and limitation (use of digital resources) specific to the online environment, which will be important considerations for future online offerings. Next steps for the current study include repeating the evaluation in the winter semester to see if opinions are stable across groups of learners and individual students.
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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| 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".