A longitudinal Q-study to assess changes in students’ perceptions at the time of pandemic
Bibliographic record
Abstract
The COVID-19 pandemic forced many universities and colleges to rapidly adopt online course delivery. As with any new foray, realizing the optimal aspects of a course to change became incredibly important for course instructors. In this study, we used a particularly sensitive method, i.e. Q-methodology, to evaluate changes based on students' perceptions from fall 2020 to winter 2021. Q-methodology is commonly used to uncover shared values, opinions, and preferences. Using Q-methodology, students participating in both semesters of an undergraduate anatomy and physiology course were surveyed in fall 2020 and winter 2021. The Q-sample included 44 statements. Data from fall 2020 were treated as the baseline and changes in students' perceptions from 2020 to 2021 were assessed. In total, 31 students completed both fall 2020 and winter 2021 course evaluations. Three salient factors emerged from the fall 2020 evaluation: Overtaxed students, Solo Achievers, and In-Person Learners. At the baseline, students were concerned mostly about the delivery of the course, then the winter 2021 evaluation showed how they were adjusting to online learning. The longitudinal Q-study proved to be robust in identifying changes in perceptions. These granular findings indicate how students might differ in viewing and evaluating online courses. This methodology can be used in redesigning and restructuring different components of an online course in higher education settings.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".