Students' perceptions of tutor feedback: a pilot study
Bibliographic record
Abstract
BACKGROUND: Feedback offered to dental students by their tutors should aim to elicit ongoing learning and motivation. Previous studies looked at the impact on learning of feedback delivered by tutors from tutors' perspectives. However, what students know about feedback and its purposes and how they experience them during their study effect the impact of feedback on learning. The aim of this pilot study was to assess the proprieties of tutor feedback and its impact on future learning from the students' perspective. METHODS: A short questionnaire based cross sectional survey was designed and delivered electronically to 135 undergraduate and postgraduate students at Brescia Dental School, Italy. The questionnaire consisted of 16 questions which were divided into 3 sections. Quantitative data were collected via Google Forms, the analysis of the data was undertaken using SPSS software, Version 24. RESULTS: Sixty-one students (45.2%) responded to the questionnaire. Forty-one of respondents (67.2%) were undergraduate students and 20 (32.8%) were postgraduate students. The vast majority of students indicated that they received feedback, thirty (49.2%) indicated that it was delivered by tutors and eight (13.1%) by fellow students. Further, students reported that feedback was timely, delivered within two weeks of assessments and that constructive criticism was the favoured feedback style (N.=52, 85.2%). Most students felt that the feedback they received helped with ongoing learning (N.=54, 88.5%). CONCLUSIONS: Most of the respondents considered that feedback received at Brescia Dental school did have a positive impact on their learning. This is of course what tutors hope would be the case but nevertheless it is gratifying to receive this endorsement from the respondent students. A more comprehensive study involving multiple dental schools in different learning environments will now be undertaken, including the collection of qualitative data.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".