An Analysis of Errors in Writing Skill of Adult Iranian EFL Learners Preparing for the IELTS
Bibliographic record
Abstract
This study sought to examine the sort, frequency, and sources of writing wrongs committed by adult Iranian EFL students. To score the participants’ written essays and speaking interviews, the four criteria specified for the IELTS Speaking and Writing Band Scores (British Council, 2014) were taken into consideration. The study also tried to comparatively analyze the error categories made by men and women learners regarding the type and frequency of their linguistic errors. To gather the information, from the population of faculty members at Hormozgan University of Medical Sciences (HUMS) in Bandarabbas, Iran, 100 adults, both male and female, with their age ranging from 31 to 52, were selected using convenient sampling. Based on their previous IELTS band scores ranging from 4 to 6, the members are separated into three groups.The results of data analysis revealed that verb tense was the very common grammatical mistake done by members in all three groups. For the cohesion and coherence and lexical sub-categories, relative clauses and incorrect use of target lexical item were regarded as the most common categories of errors. Outcomes of Chi-Square analyses also showed substantial differences among errors committed by participants in different groups. Finally, the comparison between male and female participants’ errors revealed that male participants made both written and spoken errors more than females. According to the results, recommendations, and any suggestions that are of importance to teachers and policymakers as well as to EFL learners are presented in detail.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".