Comparative Analysis of Feedback Practices and Perspectives in Online Academic Writing Assessments at Two Regional Tertiary Institutions
Bibliographic record
Abstract
This study addresses the gap in comparative analyses of feedback strategies in online writing assessments at higher education institutions in the Gulf region. Although previous research has examined different facets of online feedback, direct institutional comparisons have been overlooked. This research provides new insights into effective feedback techniques through a comparative study of feedback approaches at the Modern College of Business and Science (MCBS) in Oman and Qassim University (QU) in Saudi Arabia. The primary aim is to investigate faculty practices and perceptions regarding online feedback, evaluating its effectiveness and impact on student success. By examining how faculty members provide online feedback and its effects on students' achievements, the study seeks to enhance students' academic writing skills and mastery of disciplinary knowledge. The findings contribute to the conversation on effective feedback strategies in contemporary education, offering valuable insights for improving feedback practices, informing faculty development, guiding institutional policies, and enriching the broader literature on online teaching and assessment. The study's conclusions were based on data gathered from 41 respondents and analyzed through quantitative and qualitative methods, providing a comprehensive understanding of online feedback practices and their impact on enhancing EFL students’ writing 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 | 0.017 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".