A Comparative Study of the Effectiveness of Two Strategies of Etymological Elaboration and Pictorial Elucidation on Idiom Learning: A Case of Young EFL Iranian Learners
Bibliographic record
Abstract
This study examined the effect of etymological elaboration, pictorial elucidation, and integration of these 2 strategies on idiom learning by L2 learners. A total number of 80 homogeneous intermediate learners studying English at 3 language institutes in Isfahan, Iran, were selected. The intermediate participants were selected as the result of administering an Oxford Placement Test (OPT) to them. Then, the participants were divided into 4 groups of equal size, that is, control, etymological, pictorial, and etymological/pictorial groups. Before the experiment, all the participants took a pretest to ensure their unfamiliarity with the idioms. The idioms that were known even by 1 participant were crossed out, and finally 30 idioms were chosen for instruction. Then, the experimental groups received their relevant treatments during 15 sessions, whereas the control group learners learned idioms through definitions and example sentences. After the implementation of the experiment, the 4 groups, once again, sat for a test (i.e., the immediate posttest) to see whether the treatments had improved idiom learning. Finally, the data were analyzed by an independent samples t test and one-way between-groups ANOVA. Results showed that all the 3 strategies significantly improved the participants’ idiom learning. Results also pointed to the fact that the etymological elaboration/pictorial elucidation strategy was the most effective strategy on idiom learning.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".