Dental faculty and student views of didactic and clinical assessment: A qualitative description study
Bibliographic record
Abstract
OBJECTIVE: Although assessment is essential to accurately represent student learning, little is currently known about student and faculty perceptions of assessment in dental schools. Our study aimed to explore faculty and student views of didactic and clinical assessments in the School of Dentistry at the University of Alberta. METHOD: Qualitative description informed the study design. Data were collected through focus groups and analysed inductively using manifest content analysis. RESULTS: Five focus groups were conducted with faculty (n = 34) and three with students (n = 19). Faculty and student views of assessment were related to improvements made (perceived positive changes), improvements needed (perceived limitations) and improvements recommended (recommendations to improve perceived limitations). Faculty and students reported that improvements made (eg adequacy of assessment to students' levels of training) varied across instructors, courses and learning environments. Both faculty and students perceived clinical assessments as less appropriate than didactic assessments. Faculty perceived limitations were mostly related to assessment appropriateness, especially assessment accuracy and comprehensiveness, whilst student perceived limitations included other issues related to appropriateness (eg misalignment with course objectives) as well as issues related to assessment volume, pace and scheduling. Similarly, faculty recommendations focused on enhancing the assessment of clinical competencies, whilst students' recommendations aimed to also improve assessment scheduling, volume and usage (eg for learning purposes). CONCLUSIONS: Faculty and student views of assessment complemented one another. Our data show that assessment in dental education is multidimensional, so that multilevel strategies may be needed to improve this component of dental curricula.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".