The Big Q: Is Q‐methodology valid for evaluating a large‐scale, cross‐disciplinary anatomy and physiology course?
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Introduction Course evaluations are an important tool to gather feedback on the structure of a course, instructor effectiveness, and the overall learning experience. Critically, the Likert scale approach used by most institutions lacks course specificity and the difference between responses cannot be assumed equal (eg. “strongly agree – agree” vs. “agree – neutral”). This makes evaluating the effectiveness of a course and identifying areas that need improvement difficult. Q‐methodology is a technique that mitigates these issues by polling students for qualitative feedback statements that represent prevalent opinions of the course, then asking them to rank the statements relative to each other. Students are then clustered by shared opinions, values, and preferences. Methods This study uses Q‐methodology to assess student opinions on an undergraduate anatomy and physiology course (850 students). Specifically, students across five disciplines (midwifery, bachelor of health sciences, engineering, nursing, and integrated biomedical sciences) enrolled in the same second‐year undergraduate anatomy and physiology course were recruited into the study. All students experienced the same lecture and laboratory components as well as discipline‐specific tutorials. Students were asked to rank 37 statements relative to each other using an online platform. A by‐person factor analysis was completed using the qfactor program in Stata. Overall, the goal of this study was to validate Q‐methodology as an assessment modality across different populations experiencing the same course. Results 143 students participated in the study (70.6% female, 25.2% male, 4.2% rather not specify; median age: 19, range: 18 – 38). The by‐person factor analysis classified students into three significantly different groups (22 students unassigned) representing 1) students who greatly appreciated the use of cadaveric specimens (n = 55), 2) students who were extremely dissatisfied by the means of evaluation (n = 40), and finally 3) students who despised the virtual reality (VR) supplementary resource (n = 26). Group 1 expected a significantly higher grade than the other two groups (p<0.05). No demographic data correlated with the groups, nor did discipline. All three groups agreed upon six consensus statements. Conclusion Critically, this study uncovered three distinct opinion patterns spanning all five academic disciplines within the course. The study provided guidance for course reform and suggested that discipline does not predict course evaluations. The study supports the use of Q‐methodology analyses for assessing student opinions on a large scale. Future work looks to re‐assess student course evaluations in the same course to determine how Q‐methodology outcomes change in response to “Q”‐directed course reform.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 découle