Team‐Based Learning in Prosthodontics Courses: Students’ Satisfaction
Bibliographic record
Abstract
The goal of this cross‐sectional observational study was to assess dental students’ satisfaction regarding team‐based learning (TBL) methodology in prosthodontics courses taught at College of Dentistry, Princess Nourah bint Abdulrahman University, Saudi Arabia. Undergraduate dental students at second, third, fourth, and fifth years were taught prosthodontics courses through traditional and TBL pedagogies. TBL sessions consisted of preparation, readiness assurance, and application. At the end of each prosthodontics course, the students were asked to complete a self‐administered questionnaire that was divided into four sections to assess the effect of TBL on the following parameters: information acquisition, interpersonal skills improvement, classroom environment, and the students‐instructors interaction. The responses of the questionnaire followed the Likert scoring method (scaled from 1 to 5). The t‐test and ANOVA statistical analyses were performed using SPSS. Results. The response rate to the questionnaire was 86%. There were a significant relationship and correlation between TBL pedagogy and student satisfaction (P values ≤ 0.05) for all levels. The means of the responses for the second and fifth years were 4.36 and 4.56, respectively, where the means for the third and fourth years were 3.54 and 3.59, respectively. The parameter notably affected by TBL was interpersonal skills enhancement. All students strongly agreed that TBL enhances personal flexibility and boosts their self‐esteem. Conclusion. Students showed positive perceptions about TBL pedagogy in terms of active engagement, knowledge acquisition, and improvement of interpersonal skills leading to more efficient learning outcome.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".