Teachers’ Perceptions about Teaching Multimodal Composition: The Case Study of Korean English Teachers at Secondary Schools
Bibliographic record
Abstract
Twenty-first-century literacy is not confined to communication based on reading and writing only traditional printed texts. New kinds of literacies extend to multimedia projects and multimodal texts, which include visual, audio, and technological elements to create meanings. The purpose of this study is to explore how Korean secondary English teachers understand the 21st literacies and multimodal composition in this era of new types of communication. Framing the study are questions pertaining to what these teachers think about teaching multimodal composition in their writing classrooms. The schools of South Korea, including those in this study, prioritize high-stakes standardized tests, and teachers as well as students and parents gauge success by these test scores. As a result, teachers primarily rely on direct instruction via lectures to provide skills and knowledge to ensure that students will succeed in the high-stakes tests. So while teaching and assessment practices in the classroom still adhere to traditional approaches, ongoing technology outside school has transformed the ways in which young people – the students – generate, communicate, and negotiate meanings via diverse texts. If the primary goal of education is to teach students lifelong skills needed in society, it is the responsibility of schools and teachers to recognize social changes and promote individual learning needs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".