The Relationship Between Sense of Community and Collaborative Learning: A Quantitative Study in Medical Education (Preprint)
Bibliographic record
Abstract
Background: Medical education has shifted from an individual, teacher-led process to an interactive, group-oriented approach, fostering clinical reasoning and teamwork. Sense of Community (SoC) appears to be a key factor in this process; however, its link to collaborative learning and academic success is underexplored. Objective: This study investigates this relationship, focusing on SoC dimensions (connectedness and learning) as well as the different forms of initiative collaborative learning and subordinate collaborative learning. Methods: The German form of the Classroom Community Scale (CCS-D) was used to assess SoC. The extent of collaborative learning was measured using the Learning Strategies in Study questionnaire. A total of 331 first-year medical students participated. Data analysis included exploratory factor analysis, correlation analysis, and regression analysis. Results: SoC showed a moderate positive correlation with the use of collaborative learning strategies (r=0.466; P<.001). Connectedness emerged as a significant predictor of collaborative learning (R²=0.257, adjusted R²=0.255; F1,329=113.990; P<.001). Both initiative collaborative learning and subordinate collaborative learning are based more strongly on feelings of connectedness. The feeling that members of the course depend on oneself had the strongest predictive value for collaborative learning (R²=0.188, adjusted R²=0.185; F1,329=75.956; P<.001). Conclusions: A strong SoC promotes the use of collaborative learning strategies, particularly through social connectedness. The lesser importance of the learning dimension suggests that social bonds appear more influential than shared academic goals, particularly during help seeking, where trust and psychological safety are critical. Targeted support of learning communities may enhance didactic approaches and foster both initiative collaborative learning and subordinate collaborative learning. Cultivating connectedness and responsibility may support academic adaptation and emotional well-being in the critical early phase of medical education.
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".