Collocational Differences Between L1 and L2: Implications for EFL Learners and Teachers
Bibliographic record
Abstract
Collocations are one of the areas that produce problems for learners of English as a foreign language. Iranian learners of English are by no means an exception. Teaching experience at schools, private language centers, and universities in Iran suggests that a significant part of EFL learners’ problems with producing the language, especially at lower levels of proficiency, can be traced back to the areas where there is a difference between source- and target-language word partners. As an example, whereas people in English make mistakes, Iranians do mistakes when speaking Farsi (Iran’s official language, also called Persian) or Azari (a Turkic language spoken mainly in the north west of Iran). Accordingly, many beginning EFL learners in Iran are tempted to produce the latter incorrect form rather than its acceptable counterpart in English. This is a comparative study of Farsi (Persian) and English collocations with respect to lexis and grammar. The results of the study, with 76 participants who sat a 60-item Farsi (Persian)- English test of collocations, indicated that learners are most likely to face great obstacles in cases where they negatively transfer their linguistic knowledge of the L1 to an L2 context. The findings of this study have some immediate implications for both language learners and teachers of EFL/ESL, as well as for writers of materials.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".