The Big Q: Evaluating a Large‐Scale, Cross‐Disciplinary Anatomy and Physiology Course Using Q‐Methodology
Notice bibliographique
Résumé
Introduction Course evaluations are an important tool for students to provide feedback on the structure of a course, the effectiveness of the instructor, and the success of their learning. However, since most course evaluations use the Likert scale, it is often difficult to accurately capture the diversity of student experiences and specific areas for course improvement. An alternative approach to course evaluations is Q‐methodology which asks students to rank salient opinion statements relative to each other into a forced, normal distribution rather than independently on a Likert scale. The clustering of opinions among students performed by Q‐methodology analysis highlights groups of shared opinions, values and preferences which are useful in understanding prevalent student perspectives and driving course reform. Previous work in our lab has employed Q‐methodology to assess interprofessional education, and pathoanatomy courses. Aims In this study, we will use Q‐methodology to assess a large‐scale (class size = 850) anatomy and physiology course across five disciplines in order to 1) validate the Q‐method assessment across different populations experiencing the same course, 2) determine which laboratory experience is most appropriate for students, and 3) evaluate the equivalency of the course experience across disciplines. Methods Students across five disciplines (midwifery, nursing, engineering, iBioMed, and health sciences) enrolled in 1 st , 2 nd , and 3 rd year Anatomy and Physiology will be recruited into this study. Critically, while all students experience the same lecture, the tutorial portion of the course is varied to best suit disciplinary needs. For example, the midwifery tutorial assignment is focused on integrating anatomy and physiology knowledge with the presentation of midwifery‐related information to the general public, while engineering tutorials focus on applying biomedical engineering to the systems of the body. A Q sample consisting of approximately 40 statements will be generated from past course feedback, previous Q method studies and relevant literature. Participants will be asked to rank Q sample statements relative to each other in the second term of the course using an online “Q‐sort” platform. After data collection, a by‐person factor analysis will be completed using the qfactor program in STATA to uncover prevalent opinions within the cohort. Data will be considered across disciplines and with reference to participant demographics. This protocol has been approved by the McMaster Undergraduate Research Ethics Board and is accordance with the Declaration of Helsinki. Anticipated Significance The results of this study look to extend previous Q‐methodology work in terms of validating this Q Method practice across populations. For this course, discrepancies between tutorial experiences and best practices to support course reform will be explored. Support or Funding Information This study was supported by the Education Program in Anatomy at McMaster University. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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 | 0,042 | 0,085 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».