Teaching English Idioms to Chinese EFL Learners: A Cognitive Linguistic Perspective
Bibliographic record
Abstract
Learning idioms has always been difficult for L2 learners of English. Drawing on a cognitive linguistic perspective of idiom learning, this paper reports on an empirical study that investigated the effects of incorporating the knowledge of conceptual metaphor and metonymy in L2 classroom instruction of English idioms. The study confirmed the efficacy of applying the conceptual metaphor- and metonymy-based ways of teaching to Chinese college-level EFL learners. It further revealed the different degrees of teaching effect towards different types of metaphoric idioms. While no significant progress was made in learning orientationally and ontologically metaphoric idioms, students benefited more from the conceptual metaphor-based method in learning structurally metaphoric idioms. These findings serve to enrich L2 idiom pedagogy and provide EFL learners with strategies other than “rote memorization” in the process of idiom learning.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".