Optimizing Parent-Teacher Collaboration in Refugee Children’s Learning
Bibliographic record
Abstract
Conflicts in the Middle East have long been displacing thousands of desperate families seeking refuge, with more than half of the refugee population often comprising of school-aged children. Following the Syrian war, Canadian classrooms welcomed an unprecedented influx of Arabic-speaking learners with interrupted/ limited prior education (40,000 resettled from 2015-2016). Research about the experiences of Arabic-speaking refugees is not only essential, but necessary for establishing the educational infrastructure and support systems needed to promote integration and learning. This study examines the ways in which we can further develop refugee student learning (who arrive with unique challenges i.e. trauma, illiteracy) by optimizing parent-teacher collaboration. We conducted six focus groups with the key stakeholders in LEAD schools, a unique school system part of a larger Canadian Board supporting refugee learners exclusively: two with the LEAD parents (segregated as a culturally responsive measure), three with the LEAD teachers, and three with the Diversity and Learning Support Advisors and In-School Settlement Practitioners who are the bilingual liaisons between the schools and families. All interactions with the parents were held in Arabic, led by Arabic researchers. The results discussed the ways in which communication channels may be modified to be more accessible to families, increasing awareness on the nuance of dialect and language with Arabic learners, increasing informal opportunities for parent participation, as well as increasing administrative engagement with refugee families to better understand their needs and circumstance. This research not only explores challenges, but offers valuable insight on how the relationship may be realistically optimized.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".