Teaching English Idioms as Metaphors through Cognitive-Oriented Methods: A Case in an EFL Writing Class
Bibliographic record
Abstract
Idioms have long been regarded as problematic for L2 learners due to the arbitrariness of their meanings and forms. Traditional methods of teaching idioms focus on rote learning and memorization. Recent developments in cognitive linguistics research have considered idioms as analyzable expressions which are motivated by conceptual metaphors and whose meanings can derive from associations between source and target concepts. Believed to be imageable and comprehensive, idioms should be learned through the process of raising L2 learners’ awareness of conceptual metaphors behind these expressions. Nevertheless, these methods fail to notice culture entailments embedded in conceptual metaphors. Especially for FL learners who share neither common cultural background nor living environment with the target language users, difficulties resulting from transferability between L1 and L2 idioms owing to cultural similarities and differences may be serious. To bridge the gap between idioms and conceptual metaphors caused by cross-cultural differences, this study suggests a teaching method by incorporating the idea of metaphoric mappings. A case study was conducted in an EFL writing class to investigate the effect of the methods. Essays written by the students before and after the instruction were analyzed. Results showed that the students increased frequencies of using not only common idiomatic expressions but also creative analogies comprising vivid images based on the conceptual metaphors taught. Additionally, they used L2 expressions whose conceptual metaphors also existed in L1 more often then expressions whose conceptual metaphors were exclusive in L2. Such findings affirm the importance of culture and provide valuable insight to EFL teachers in adopting cognitive-oriented method to teach English idioms.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".