Using Q Methodology to Evaluate Online Anatomy Education: Learning in a COVID‐19 Context
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,037 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».