Developing and Testing a Framework for Learning Online Collaborative Creativity in Medical Education: Cross-Sectional Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".