Uncovering the Perceptions of Grades in Dentistry Students: How are Grades Impacting Learning & Clinical Development?
Bibliographic record
Abstract
Purpose. Educators have noted that grades may have negative impacts on students’ clinical performance; however, dental students’ perceptions of grading practices remains unknown. This study aims to: (i) identify the most prevalent grade perceptions and the underlying driving factors, and (ii) explore how students perceive the impact of grades on their learning and clinical development. Methods. To address our aims, we used a mixed-methods research approach with a population of dentistry students at our institution. We distributed online surveys (Winter 2021; n=68), and conducted virtual semi-structured interviews (Fall 2021; n=6) and a focus group (Fall 2021; n=3). Qualitative data was analyzed through an inductive and deductive thematic process. Results. Sixty-eight students participated in the online survey (26% response rate), and nine students participated in the interviews and focus group. Dentistry students predominantly perceived grades as a ranking tool to competitively distinguish themselves from their peers (29% of quotes), driven mainly by residency and specialty admission processes (34%). Percentage grades were also perceived as a barrier to learning and ineffective at guiding clinical development. Respondents identified that grades distracted from learning and did not indicate whether a student had achieved a clinically acceptable level of performance. Conclusions. Given these findings, grading practices in dental education should be further evaluated. Some students perceive that percentage grades are more harmful than helpful for professional development. Further, participants suggested that ordinal grading may be more beneficial for evaluating clinical competence. This study provides important considerations for clinical educators and academic institutions wishing to reflect on the use of grades as a conventional academic metric.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".