Supporting refugee- and migrant-background students in a Canadian elementary classroom : challenges and promising teaching practices
Bibliographic record
Abstract
Canada has a long history of resettlement of refugee and protected persons, and between 2015 and 2019, over 225,000 were resettled (IRCC, 2020). Many refugee background newcomers to Canada (42%) are school-aged children and youth, including students with limited or interrupted formal education (SLIFE) (IRCC, 2017); many have experienced triple trauma due to forced migration, during transition, and upon resettlement in Canada (Stewart et al. 2019). This lack of opportunity to attend school and traumatic experiences presents daunting challenges for refugee-background students and their teachers who may lack resources and preparation to meet their complex needs (Stewart et al., 2019). This study seeks to contribute to better understandings in this area through its exploration of what an expert elementary school educator, together with her team-teaching colleagues, perceived as the challenges and successful approaches to language and literacy education for Grade 6/7 refugee-and-migrant background students (RMBS). The study also explored the potential of multiliteracies pedagogies to leverage the multimodal communicative repertoires of RMBS, as they engaged in a cross-curricular unit of study in their mainstream Grade 6/7 classroom. The theoretical frameworks drawn from were a socio-cultural perspective of literacy, multiliteracies pedagogy and learning by design, as well as conceptions of identity and investment. Data was gathered through field notes, participant observation, audio recording of classroom interactions, student artifacts and texts, and semi-structured focus groups and teacher interviews. The data collected was inductively and deductively thematically analyzed. Findings illuminated the teaching team of expert educators’ perceptions of the challenges of working with RMBS students, as well as successful educational approaches to support RMBS and enhance their achievement. The findings also contributed to a better understanding of the development of innovative pedagogical practices that engage and enhance these youths’ full communicative repertoires and identities towards academic achievement, social and emotional learning, and literacy engagement.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".