Saudi Graduate Students’ Perceptions Toward Automated Writing Feedback for Improving Academic Writing
Bibliographic record
Abstract
Over the last few years, we have witnessed a growth in the studies that focus on the feedback on writing in a second language, including computer-based feedback (Zhang & Hyland, 2018). Automated writing feedback refers to the immediate feedback generated by computers to correct or improve the text. The current study discusses the perceptions of Saudi graduate students toward using automated writing feedback to improve their academic writing. The design of the research is a quantitative survey study. A questionnaire consisting of 13 items is the instrument for data collection. The sample size is 46 (male and female) Saudi graduate students. By using descriptive statistics, the findings revealed positive perceptions of the use of automated writing feedback tools. The findings of this study could shed light on the importance of conducting more research on the impact of automated writing feedback in one particular writing aspect, such as organization, content, coherence, unity, or style.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".