Idiomaticity as a Language Learning Barrier: The EFL Context of Saudi Arabia
Bibliographic record
Abstract
The aim of this paper is to investigate the impact of Idiomaticity on language learning and the extent to which it can be a language learning barrier. It contrasts the perspective of language teachers and the attitude of language learners regarding how idioms can influence language learning. The theoretical framework provides a description of the general properties of English idiomatic expressions and shows the relevance of idiomaticity to linguistic theory. The paper is based on an analytical analysis and follows a quantitative approach in which two questionnaires are used to collect the data. The two questionnaires are administered to two independent samples: 20 participants representing ELT teachers at the tertiary level and 80 subjects representing Saudi EFL college students. The data are then analyzed using SPSS (Statistical Package for the Social Sciences). The study reveals learners’ reasonable consensus on the issues assessed. They generally acknowledge the significance of idioms for language learning but with a general dissatisfaction with their status in learning and teaching contexts. Both teachers and learners view idioms as odd pieces of language that lack a uniform character and do not receive due attention in language syllabi and curricula. Teachers give different ratings on the pedagogical value of idioms, but they generally show low interest in teaching them.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".