The Effects of Electronic Feedback on Medical University Students’ Writing Performance
Bibliographic record
Abstract
This study was designed to see whether electronic feedback positively affects medical students’ academic writing performance. Two groups of medical university students were randomly selected and participated in this study. In order to see whether the provision of electronic feedback for the compulsory academic writing course for medical students is effective, the researchers divided 50 medical students to the traditional (n=25) and intervention groups (n=25). Pre-test and post-test were conducted at the beginning and at the end of the semester. Electronic feedback was given to the medical students in the intervention group, while the medical students in the traditional group received the traditional pen and paper feedback. By comparing the scores of two written assignments at the beginning and the end of the semester, regarding the application of electronic feedback, the results showed that not only medical students’ overall writing performance improved after providing them electronic feedback, but every single writing component was also enhanced after the intervention. There was a significant difference in the post-test academic writing scores between the traditional and intervention groups (P < 0.001). This difference was not significant in our control group who was given pen-and-paper feedback. In terms of specific writing components, the most affected components in this approach were content followed by organization, language use, vocabulary, and sentence mechanics, respectively. Although this study focused on medical students’ academic writing ability and reported the effect of electronic feedback on medical students’ writing performance, electronic feedback can be equally beneficial for enhancing student-practitioners’ practical clinical skills.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".