Core-periphery assessment of collaboration for knowledge building and translation in continuing medical education
Bibliographic record
Abstract
Collaborative assessments have potential to support sociocognitive interactions that foster a shift from traditional educational models toward collective knowledge innovation networks. This study illuminates relationships between pre/posttest assessment and social network core-periphery analytics, verifi ed by content analysis, and demonstrates changes in positions/roles and the co-creation of ideas for translation to practice. Core-periphery analytics extends Freeman’s concept of centralization to shared leadership and is wellaligned with Knowledge Building theory. Family physicians in the End-of- Life Care Distance Education Program, a 5-month, online continuing medical education course, participated in this study. Core-periphery analysis of Knowledge Forum® build-on measures were correlated with individual pre/posttests results to provide structural visualizations of collaboration, across 5 modules. In both groups, participants with strong prior knowledge and pre/posttest gains shared core position/leadership roles with the facilitator. Thematic analysis of discourse identifi ed numerous emergent ideas and Knowledge Building trajectories, beyond module objectives – evidence of participant metadesign. This study provides a model of new possibilities for collaborative assessment and educational design to facilitate a shift from learning, as an exclusively individual enterprise with external assessment, to the creation of a community with participants assuming agency for the emergence of relevant issues and authentic, meaningful problems, scaffolded by transformative assessments – integral to Knowledge Building and creation.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".