A Bigger Q ‐ Evaluating the impact of pandemic‐related course changes on students’ learning experiences
Notice bibliographique
Résumé
Background There is no clear way to know how changes in a course have impacted the student learning experience. In a recent series of studies, our group established Q‐methodology as a robust alternative to traditional Likert‐scale course evaluations. Through utilizing a revised statement ranking system, and combining qualitative and quantitative analysis methods, Q‐methodology mitigates issues of disparate feedback, and averaged scores which make Likert‐scale data difficult to interpret and act upon. Our previous work has demonstrated that students can be statistically grouped based upon shared opinions, preferences and values, and that evidence‐based course reform decisions can be made with this information. In a remarkable year, requiring significant and rapid adjustment to accommodate pandemic‐related measures, understanding how students perceived their course experience is important not only to examine outcomes but ensure future evidence‐based changes are possible. Objective Through sequential Q‐methodology course evaluations, this study evaluates how course changes, made in response to emergency pandemic teaching/learning, alter students’ perceptions of their course experience. Methods In 2019 and 2020, students in a multi‐disciplinary undergraduate anatomy and physiology course were asked to complete the same Q‐methodology course evaluation, ranking 37 statements relative to each other at year end. Between academic years, significant changes in course delivery (laboratory and tutorial sessions moved online) as well assessment modality (multiple choice exams changed to a group assignment) were made. Data from both 2020 (n=64) and 2019 (n=125) were analyzed together via by‐person factor analysis, and factors were interpreted using Q‐methodology conventions. Participant distributions across the factors were compared between cohorts (2020 vs 2019) and across demographics via Pearson's Chi‐square test. Results A three‐factor solution statistically grouped students with a generally 1) positive, 2) negative and 3) neutral disposition toward the course. Those in the 2019 and 2020 cohorts were significantly differently distributed amongst the factors (p = 0.006), such that a greater proportion of the 2020 vs 2019 cohort (58% vs 37%) expressed dissatisfaction with the course. Primarily, dissatisfaction surrounded issues with course structure and assessment – two aspects which were significantly altered in response to the 2020 pandemic. Demographics were not significantly different across factors. Conclusion Clear evidence for course reform relies upon knowing what worked, and what didn't. In this case, while a negative shift in disposition seems unremarkable, Q‐methodology uncovered key elements contributing to student dissatisfaction which are important to consider as programs consider online‐learning as a long‐term option.
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 | 0,021 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| 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) | 0,015 | 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 ».