Comparison Study: The Impact of Lecturer’s Feedback on EFL Students’ Essays
Bibliographic record
Abstract
This study explored students’ grammatical, mechanical, and lexical errors in EFL writing. Also, it aimed to investigate the effect of instructor feedback throughout the semester on students’ types and frequency of errors in two types of essays, including a process essay and an argumentative essay. This study was conducted on 24 EFL students studying in their first year of college in the applied linguistics department. To achieve the purpose of the study, the researcher used a descriptive qualitative study that dealt with document analysis. The author analyzed ten documents to understand how teacher feedback could develop students’ levels in the target language and increase their abilities to properly use the grammatical, mechanical, and lexical rules in academic writing. Thus, the researcher used five written samples of students’ process essays and five written samples of their argumentative essays to compare and find out about students’ academic writing progress. The findings of this study revealed that the instructor’s feedback positively impacted students’ writing development and gradually helped them overcome the committed errors. There were significant differences in students’ writing samples before and after the instructor’s feedback. Therefore, EFL students’ writing of the argumentative essays showed noticeable progress in students’ language use and a reduction in the number of errors that students committed in their process essays.
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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 | 0.011 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".