Written Corrective Feedback as Practiced by Instructors of Writing in English at Najran University
Bibliographic record
Abstract
The present study aimed to address the extent to which faulty members and students at the department of English language at Najran University practice using the ways of written corrective feedback. The questionnaire, as the main study instrument was used to collect data while the descriptive analytical approach was used to analyze these collected data. Findings revealed that the direct way of correction, i.e., the identification of student’s errors by underlining or circling and then telling them how to correct such errors without allowing them the chance to figure out what the corrections are, was the most practiced way of written corrective feedback. Using Arabic, as it was students’ mother tongue, to show them their errors and then explain to them how to correct these errors was the second practiced way. Indirect correction like for example correcting student’s errors through writing in the margin that there was an error without giving them the correct answer was the least used way, as indicated by faculty members. Nevertheless, correcting students’ errors by coding the exact error in the text without giving them the correct answer was the least used way from students’ viewpoint. Moreover, findings showed that both faculty members and students were in favor of the comprehensive not the selective way of correction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".