Research into practice: Digital multimodal composition in second language writing
Bibliographic record
Abstract
Abstract Digital multimodal composing (DMC) has been valued as an engaging pedagogy in language teaching and learning in recent decades. Although research on DMC is flourishing and evidences its benefits for students' development as second language (L2) users and writers, there are some missing links between research findings and classroom practices. In this article, we examine three kinds of relationships between research and practice with regard to DMC: areas in which research findings have not been well applied, areas in which research findings have been reasonably well applied, and areas in which research findings have been usefully applied. As recent research–practice frameworks in education research emphasize a collaborative relationship between researchers and practitioners, we argue that L2 writing researchers' and teacher educators' reflections and experiences are crucial to facilitate the dialogue between DMC research and practice in writing contexts. We suggest that DMC should be incorporated into L2 teacher education programs so that instructors are equipped with the necessary knowledge and competence to design, implement, and assess students' DMC productions.
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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 | 0.039 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".