Bibliographic record
Abstract
This research paper aims to test the extent to which 100 Kuwaiti EFL learners are aware of the correct use of inflectional morphemes in English. It also explores the main causes of the errors that Kuwaiti EFL learners may make. Additionally, it checks whether the English proficiency level of the participants plays a role in their answers on the test. To this end, a multiple-choice test was used to measure the participants’ ability to use the correct inflectional morphemesin English. Following data analysis, the results reveal that Kuwaiti EFL learners are aware of the correct use of the inflectional morphemes in English to a certain degree (total mean=65.5%). Additionally, the t-test shows that the participants’ English proficiency level plays a central role in their comprehension of these morphemes. In particular, there is a statistically significant difference between the answers of the advanced learners (ALs) (73.5%) and intermediate learners (ILs) (57.5%). The number of correct answers provided by ALs is higher than that provided by ILs. Regarding the types of errors made by the participants, it has been argued that the most noticeable ones are due to first language (L1) negative transfer and the irregularity of some types of inflectional morphemes in English. Finally, the study concludes with some pedagogical implications and recommendations for further research.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".