Social and Emotional Learning Difficulties of Refugee High School Students in an After-school Tutoring Program
Bibliographic record
Abstract
School-aged children constitute a significant portion of the large number of refugees who have resettled in Canada in recent years. Due to the lack of cross-cultural competencies, a social justice focus, and transformative leadership skills, Canadian schools are often challenged to effectively address refugee students' socio-psychological problems. Moreover, educational literature and policy, which specifically target Canadian refugee students, are scarce. To help with the issue, this study examined eight refugee high school students through an online after-school tutoring program and evaluated their performances in the five domains of social-emotional learning competencies: social awareness, self-management, relationship skills, responsible decision making, and social awareness. The two researchers participated in this study as tutors and adopted observation as the main approach. Findings of the study revealed that refugee students' performances in these skills was not optimal, in general. Especially, there is a high demand in improving the refugee students' self-awareness, self-management, and responsible decision-making. Most of them had good relationship skills as well as social awareness. Also, all the social-emotional learning skills connect closely with the refugee students' academic success.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".