Learning by Concordance as a Tool for Paediatric Dental Traumatology Education
Bibliographic record
Abstract
INTRODUCTION: A learning by concordance (LbC) tool including 33 vignettes was developed jointly by teachers from the Y and the University of X to train undergraduate dental students in paediatric dental traumatology. The aim of this work was to present a learning tool not yet described in the community of dental educators. The method was presented on two different electronic platforms to two groups of students. Different modalities were compared. METHODOLOGY: International panellists were asked to detail their reasoning for resolving ambiguous or complex situations described in clinical vignettes. Two groups were approached: a first group composed of students new to LbC (Y group) and a second group that had already experienced this type of learning method (X group). The modalities of training management differed according to the groups: Y group used the Wooclap platform and responded on a 5-modality Likert scale, while X group used Moodle and a 3-modality Likert scale. Student volunteers were able to complete a qualitative survey about the training. The main indicator used was students' opinions and feelings about different aspects of the tool. RESULTS: The training was completed by 121 students, 53 of whom agreed to give their opinion on the tool. Consistent with current knowledge, we found that novices had difficulty answering a 5-modality Likert scale because of the subtle difference between two close answers. CONCLUSION: This is the first study to introduce a LbC tool in dental education and the results showed a strong interest in this type of pedagogical tool, regardless of the online platform used.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".