A Qualitative Investigation of the Factors that Enhance, Impede, and Require Attention for the School Success and Engagement of At-Risk Newcomer Students
Bibliographic record
Abstract
Little is known about best practices to support newcomer students with a history of emotional, behavioral, or learning challenges in their pursuit of school success and engagement. The Enhanced Critical Incident Technique was employed to explore the helping, hindering, and desired practices among nine teachers who have successfully supported at-risk newcomer youth in their educational pursuits in Canada. Results revealed 64 helping and 43 hindering factors as well as 27 wish list items related to participants’ experiences of supporting newcomer students in their school success and engagement. Recommendations are made at three levels: individual, curricula, and systemic. Keywords: newcomer youth, school disengagement, school re-engagement, academic success, qualitative research, enhanced critical incident technique On sait peu de choses sur les meilleures pratiques pour soutenir les élèves nouvellement arrivés et ayant des antécédents de problèmes émotionnels, comportementaux ou d'apprentissage dans leur quête de réussite et d'engagement scolaires. La méthode améliorée des incidents critiques a été utilisée pour explorer les pratiques positives, négatives et désirées chez neuf enseignants qui ont soutenu avec succès des jeunes nouveaux arrivants à risque dans leur parcours scolaire au Canada. Les résultats ont révélé 64 facteurs positifs et 43 facteurs négatifs ainsi que 27 éléments désirés liés aux expériences des participants en matière de soutien aux élèves nouvellement arrivés dans leur réussite scolaire et leur engagement. Des recommandations sont formulées à trois niveaux : individuel, curriculaire et systémique. Mots clés : jeunes nouveaux arrivants, désengagement scolaire, réengagement scolaire, réussite scolaire, recherche qualitative, méthode améliorée des incidents critiques
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".