Digital Storytelling With Youth From Refugee Backgrounds: Possibilities for Language and Digital Literacy Learning
Bibliographic record
Abstract
Abstract This study addresses the urgent need to develop innovative pedagogies that build upon and enhance the digital literacies and representational practices of culturally and linguistically diverse youth from refugee backgrounds. In Canadian high schools, this population of students enter school with varying levels of literacy in their first language(s), as well as potentially difficult experiences due to their forced migration. For many, learning English, may become a formidable challenge. A growing corpus of case studies is beginning to show how pedagogies that draw on youths’ everyday meaning making, including their digital literacies, can effectively engage English learners in academic learning. In this qualitative, ethnographic case study involving nine youth in an English language learning classroom, we addressed the question: What is the potential for digital storytelling to draw from the fuller context of the lives and literacies of youth from refugee backgrounds to enable more autonomous language learning and identity affirmation? Our study is informed by interrelated conceptual frameworks: learner autonomy; investment in language and literacy learning; and digital literacies. Using thematic and multimodal/visual analysis, data were collaboratively coded to identify four interweaving themes: 1) use of multimodal meaning making to communicate complex, critical understandings; 2) emergence of digital literacies; 3) challenges of communicating in digital spaces; and 4) investment in identity affirmation in language learning. Implications focus on how digital storytelling as an innovative pedagogy has the potential to create space within the curriculum for stories that have deep meaning for learners.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".