Promoting the Social Inclusion of Newcomer Emerging Adults in Canadian Post-Secondary Schools: An Online Social-Emotional Learning Pilot Study
Bibliographic record
Abstract
Inclusive education plays a critical role in addressing the diversity of needs of all studentsregardless of disability or background (UNESCO, 2019).It promotes positive academic outcomes for newcomers (i.e., immigrant/international students; Weine et al., 2013), and for domestic students (Graham, 2018).However, despite the rise in migration (IOM, 2020), and the growing number of newcomer students in Canada (IRCA, 2022), there is still a gap in understanding how to promote their social inclusion (Bossaert et al., 2011).Social-Emotional Learning (SEL) has been identified as a key factor for promoting social inclusion (Weissberg et al., 2015), and for addressing common challenges to newcomers (Agnafors et al., 2021) such as improving school engagement (Zins et al., 2004), and reducing students' stress (Payton et al., 2000).This study integrated the literature on social inclusion and SEL to evaluate the effectiveness of a two-month online SEL program in promoting the social inclusion of newcomer students in Canadian post-secondary schools.This study assessed (a) changes over time in social inclusion, school engagement, and academic stress; and explored (b) how changes in social inclusion predicted changes in school engagement and academic stress.Participants included post-secondary students in Canada (N = 40, 50% control).They completed pretest-posttest and follow-up measures on their perceived levels of social inclusion (i.e., relationships, interactions, consciousness, acceptance), school engagement, and academic stress.Growth mixture modeling results showed that program participants experienced a significant increase over time in relationships, interactions, and acceptance, while social consciousness decreased.Both program participants and control group showed a decrease in academic stress, with no significant change in school engagement for either group.Further structural equation modeling results showed that among program participants, changes in relationships increased school engagement and
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".