One Does Not Simply Teach Idioms: Meme Creation as Innovative Practice for Virtual EFL Learners
Bibliographic record
Abstract
To maximize the advantages of virtual learning, the present study highlights the potential for Internet meme design and creation in English language learning (ELL) courses as an innovative activity that raises student agency, increases multimodal literacy, inculcates intercultural communication, and teaches idiomatic expression. Memes resonate a multimodal feedback loop of popular culture. In the context of language education, multimodal literacy is a necessity for 21st-century education because the affordances of digital learning platforms present the world told alongside the world shown. While some studies feature the usefulness of memes in English as a foreign language (EFL) learning, none have underscored meme creation as a learning activity. To demonstrate the activity in situ, a vignette at two Korean universities features two instructors who ask their respective students ( N = 49) to design one meme using an idiom discovered in their ELL materials from a prescribed list, then asks: 1) What common power relations and ideologies emerge in the multimodal discourse of the collected pool of student “idiomemes”? 2) What do the findings tell us about student attitudes and engagement with the activity? 3) What do the findings tell us about the importance of multimodal discourse in EFL learning? Using a multimodal critical discourse analysis of the student-created Internet memes, the findings reveal that students chose culturally familiar images to complete the assignment, suggesting that their engagement and understanding of multimodal, English discourse increases commensurately with content intuitive to their culture. The implications suggest that empowering students with a measure of agency in expressing culturally relevant, multimodal discourse in ELL course content increases their engagement in virtual classrooms. Designing idiomemes, as a virtual learning activity, is further explored as a curricular augmentation that increases the value of a student's language-learning investment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".