Dental students’ perceptions of the wildcard as a novel teaching technique in case‐based learning
Bibliographic record
Abstract
OBJECTIVE: Cases used in case-based learning should be realistic, relatively difficult, engaging, and educational to maximize clinical knowledge and skills. Data are needed to support the effectiveness of existing and new techniques to ensure these case attributes. The purpose of this study was to explore dental students' perceptions of the wildcard technique in case-based learning. This novel technique aims to ensure key case attributes by adding new information to the analysis of a case that challenges the initial diagnosis and/or treatment plan. METHODS: Constructivism (paradigm) and interpretative description (approach) informed the study design. Participants were 21 third- and fourth-year dental students who took part in an oral pathology seminar in which the wildcard was employed. Data were collected through individual, semi-structured interviews that were digitally recorded and transcribed verbatim. Inductive, manifest thematic analysis was used to analyze the data. Several verification strategies were implemented to ensure rigor throughout data analysis. RESULTS: Identified themes suggest that students perceived the wildcard as a new scenario that simulated clinical practice regarding settings, situations, conditions, and required skills. They also enjoyed the wildcard and found it effective in terms of knowledge acquisition, skills development, and engagement. Students valued and recommended wildcards that were challenging, authentic, and educational. CONCLUSIONS: Students positively valued the wildcard, which seems to ensure several case attributes. Learning and behavioral outcome evaluations are needed to further establish the effectiveness of the wildcard in case-based learning.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".