The Impact of Pictorial Cues on Understanding Idioms Among Arab EFL Learners
Bibliographic record
Abstract
Idioms vary extensively in their difficulty, especially for foreign language learners. English Foreign Language (EFL) learners often find transparent idioms, such as break someone’s heart, which means to make someone feel deep sadness, more straightforward. Whereas, they may find kicking the bucket ‘to die’ somehow opaque and challenging. The study investigates the extent to which Arabic speakers of English find contextual and pictorial cues embedded in social media platforms beneficial for understanding idioms and the methods they often use to comprehend idioms. Thirty female Arabic-speaking learners of English at a high school in Jeddah, Saudi Arabia participated in this study. The study used a three-version design to assess the participants’ understanding of idioms through a multiple-choice interpretation task. The participants were divided into three groups and received the same amount (n = 24) and type of idioms in three different methods: contextualization (i.e., participants were exposed to idioms in context), decontextualization (i.e., participants were exposed to idioms out of context) and the third group was exposed to pictorial-cued idioms. The findings revealed that visualisation was the most effective method for mastering idioms rather than contextualisation. On the other hand, decontextualisation was the least effective method. The study concludes with specific reference to some pedagogical implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".