Examining the Learnability of English Relative Clauses: Evidence from Kuwaiti EFL Learners
Bibliographic record
Abstract
<p>This study aims to test the extent to which 120 Kuwaiti EFL learners are aware of the structure of relative clauses in English through measuring their ability to produce this structure. It also checks whether the English proficiency level of the participants plays a role in their answers on the test. For this purpose, a sentence combination task was used to measure the participants’ ability to produce correct relative clauses in English. This test was selected in order to examine the types of error made by the participants pertinent to relative clauses in English. The results reveal that Kuwaiti EFL learners may not be fully aware of the formation rules of relative clauses in English (total mean=60.4%). The t-test shows that the English proficiency level affected the participants' answers on the test. Specifically, there were statistically significant differences between the answers of the advanced (76.3%) and intermediate learners (44.4%). The number of errors made by the advanced learners was less than that made by the intermediate learners. Regarding the types of error made by the participants, the most noticeable ones were: deletion of the relative pronoun, wrong relative pronoun, repetitive use of resumptive pronouns, passivisation of the relative clause and problems with the indirect object and genitive relative clauses. The researcher analysed the participants' erroneous answers and provided potential reasons to account for them. Finally, the study concludes with some pedagogical implications and recommendations for further research.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".