The Use of Grammatical Collocations by Advanced Saudi EFL Learners in the UK and KSA
Bibliographic record
Abstract
This study attempts to investigate the production of English grammatical collocations amongst Saudi students majoring in English in the KSA and those in the UK. It also shows the most frequent types of errors that may occur as well as some possible reasons for their occurrence. For this purpose, the researcher analysed essays written by the participants. The results reveal that Saudi EFL learners in the UK do grammatical collocation errors less than those who learn English in the KSA. Additionally, the highest number of errors in both groups was recorded on the grammatical collocations patterns, noun + preposition and adjective + preposition. It seems that L1 interference plays a crucial role in students' erroneous responses, especially those which contain a preposition. For instance, the majority of noun + preposition, adjective + preposition and preposition + noun are used incorrectly throughout the essays. Furthermore, the avoidance phenomenon in SLA may be used by Saudi students. They tend to avoid using some grammatical collocation categories such as adjective + that- clause and noun + that-clause since they are beyond their English proficiency level. Finally, the lack of knowledge of grammatical collocations is another possible reason behind such errors. Educational leaders, curriculum designers and teachers need to shed light on these types, especially as the English language curricula used in the KSA do not pay a great deal of attention to grammatical collocations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".