Developing and Testing a Framework for Learning Online Collaborative Creativity in Medical Education: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Collaborative creativity (CC) is a social process of generating creative and innovative solutions to real-world problems through collective effort and interaction. By engaging in this process, medical students can develop abilities and mindset for creative thinking, teamwork, interdisciplinary learning, complex problem-solving, and enhanced patient care. However, medical students have demonstrated limited creativity, constrained by existing pedagogical approaches that predominantly emphasize knowledge outcomes. The increasing complexity of health care challenges necessitates a pedagogical framework for medical students to foster CC in a rapidly evolving professional environment. Objective: This study aimed to develop, test, and evaluate a new Framework for Learning Online Collaborative Creativity (FLOCC). Methods: FLOCC builds on established pedagogical approaches such as design thinking and integrates sociocultural learning methods (team-based learning [TBL] and problem-based learning [PBL]). It includes 4 individual asynchronous activities (empathy map, frame your challenge, turning insights into how might we questions, and individual brainstorming) and 5 collaborative synchronous activities (bundle ideas, list constraints, final idea, prototyping, and blind testing). In this cross-sectional study, 85 undergraduate medical students participated in 2 separate studies (study 1, n=44; study 2, n=41) involving health care and engineering sustainability problems. Learner acceptability was measured using a 31-item survey (using 7-point Likert scale) consisting of 4 factors (distributed creativity, synergistic social collaboration, time regulation and achievement, and self and emotions) and 3 free text questions. Free-text comments were subjected to the inductive thematic analysis. Results: Most students were positive about FLOCC, with distributed creativity and synergistic social collaboration factors receiving the highest mean percentages of "'Agree" (78/85, 92% and 75/85, 88%, respectively). These were followed by time regulation and achievement factor (68/85, 80%) and the self and emotions factor (59/85, 70%). Only time regulation and achievement was statistically significant (P=.001) between means of studies 1 and 2. Thematic analysis revealed 4 themes such as learning experiences, collaborative responsibilities, perceived skill development, and technical challenges. Conclusions: With effective time management, FLOCC shows potential as a framework for nurturing CC in medical students. Medical schools could provide the opportunity and environment that supports creative thinking; therefore, creativity-focused approaches could be integrated into the curriculum to encourage a culture of creativity for breakthrough solutions by future doctors.
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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 | 0,045 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».