Improving effectiveness of dental students’ feedback and course evaluation
Bibliographic record
Abstract
Dental students providing feedback about a course they take, in a timely manner, benefits not only teachers, but also indirectly the students themselves, especially if given with confidence in a constructive manner. Therefore, the aim of this study was to train students on how to give feedback, to ask them to provide feedback before and after the instructions were given, and analyze the change in their responses. Participants were students who attended the second-year preclinical course in prosthodontics. They were asked to provide feedback anonymously with online surveys after completing modules of the course during the academic year. There was no intervention prior to the first feedback; however, before providing the second feedback, students were asked to read a 1-page handout related to feedback modalities. Following this, an interactive workshop in feedback was provided prior to the third survey. The received responses were ranked as either: neutral, positive, negative, or constructive and were analyzed using a mixed repeated measures test with Bonferroni correction at a 0.05 significance level. The results showed a higher number of constructive and positive responses than both neutral and negative feedback (P ≤ 0.05) within the same surveys, but no interaction effect was found between the surveys (P = 0.076). Our data showed an increase in constructive feedback provided by students after the 2 different training methods, but the modality of delivery did not seem to significantly influence the results. In summary, training students on how to provide constructive feedback may be beneficial for teachers to improve their courses.
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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 | 0.031 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".