Automated Diagnostic Feedback vs. Self‐Assessment: Rethinking Feedback Mechanisms on Academic Writing Development
Bibliographic record
Abstract
Abstract As international students increasingly pursue higher education in English‐dominant countries, developing their academic writing skills is crucial. However, limited access to individualized feedback remains a challenge. AI‐driven tools and self‐assessment offer promising solutions, making feedback more accessible. This study involved 50 international graduate students who spoke English as an additional language, randomly assigned to two groups: one received BERT‐generated automated diagnostic scores and feedback, while the other engaged in self‐assessment. Using a sequential explanatory mixed‐methods design, this study investigated the effects of automated diagnostic feedback and self‐assessment on students' academic writing performance and self‐perceived writing abilities. It also explored how cognitive, metacognitive, behavioral, and affective engagement with feedback varied across the two groups. Results indicated that the machine feedback group significantly outperformed the self‐assessment group in task fulfillment, organization, and total writing scores, while no significant differences were observed for vocabulary and grammar. Additionally, the machine feedback group demonstrated deeper metacognitive, behavioral, and affective feedback engagement. However, they also reported a decline in academic writing self‐confidence. While automated diagnostic feedback proved more effective than self‐assessment in enhancing academic writing skills, its potential negative impact on students' confidence highlighted the need for future research to balance precision with emotional support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".