Factors Influencing Dental Educators As They Develop Problem‐Based Learning Cases
Bibliographic record
Abstract
In problem-based learning (PBL) environments, patient cases encourage students' development of critical thinking and problem-solving. Previous research has found that non-structured patient cases fostered students' critical thinking and problem-solving abilities; however, structured cases dominate in dental PBL. The aim of this study was to explore factors influencing educators as they developed cases for a hybrid PBL dental education program in Canada. In this phenomenological study, semi-structured interviews were used to collect seven educators' experiences with PBL case development. Content analyses with conceptual mapping were triangulated with field notes, researcher memos, and member checking to elucidate codes and themes. There were two major themes and 14 subthemes. The major theme-external factors-involved environmental parameters that influenced educators to develop PBL cases with a definitive problem-solving approach and preferred solution. Structured PBL cases dominated because of limited curricular time for students to explore identified learning issues within a three-session framework. The hybrid PBL dental curriculum further influenced educators to develop structured PBL cases such that content was not duplicated by corresponding lectures. The second major theme-internal factors-encompassed the educators' beliefs and values about teaching and student learning. These educators were enthusiastic about PBL as an instructional strategy, but did not appear to support the PBL philosophy wherein students engage in self-directed, self-exploratory learning. Structured PBL case development occurred when educators believed students needed content expert guidance. Structured PBL cases dominated in the hybrid PBL program because the educators felt students needed guidance in solving the cases to meet the learning objectives within the limited curricular time.
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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 | 0.006 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".