Teachers’ perceptions of challenges in digital multimodal composing with newcomer adolescent students from refugee backgrounds
Bibliographic record
Abstract
Research has shown ways in which digital multimodal composing (DMC), defined as the use of digital tools to make meaning with multiple modes (e.g. languages, visuals, sounds, gestures), including video production, can empower adolescent newcomers from refugee backgrounds in school settings. However, few studies have examined teachers’ challenges with these pedagogies, particularly involving refugee-background learners, some of whom may have experienced significantly interrupted formal education. Comprehensively understanding teachers’ perceived challenges with pedagogies involving DMC to help meet these learners’ needs is a particularly urgent objective in Canada, which is increasingly committing to refugee resettlement. This qualitative case study explored teachers’ perceived challenges in DMC with newcomer adolescent students from refugee backgrounds in a secondary school setting. Guided by a multimodal approach to literacy and an identity investment perspective on participation in learning, reflexive thematic analysis led to the identification of the following teacher challenges: navigating expectations and required scaffolding, mitigating risks associated with difficult knowledge, and students’ ‘cloak of invisibility’. The study contributes an in-depth discussion of these patterns, including possible implications, to better empower educators and teacher educators to address the needs of adolescent newcomer learners from refugee backgrounds in an increasingly complex language and literacy landscape.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".