Investigating and Analyzing ESP College Students’ Errors in Using Synonyms
Bibliographic record
Abstract
This study aims to investigate and analyze the errors of English for specific purposes college students in using synonyms. It also discovers the difficulties that faced ESP students in using synonyms. A descriptive-analytical method was used in this study. The population of the study were (60) ESP college students from the college of Political Sciences at Al-Nahrain University, in the academic year 2018–2019. The sample consists of (50) ESP for college students, which were chosen randomly. Data for the study were collected from the written test which consisted of different five questions and each question contains five items, so the total items were 25. The findings of the study showed the importance of error analysis for the learners and teachers; it can provide a good methodology for investigating learners’ errors in English. The study discovered that the most occurred errors are due to their limited knowledge of acquisition of vocabulary especially for learners who study English for specific purposes. The insufficient of vocabulary knowledge causes many difficulties for learners in choosing the correct synonyms and, hence, they committed errors which prevent their advance in learning natural English. Finally, the study concludes with some recommendations for further research.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".