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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".