The Effects that Family Members and Peers Have on Students' Decisions to Drop out of School.
Notice bibliographique
Résumé
In 2003, a study of two Canadian adult literacy programs included 37 learners who revealed a variety of reasons for having dropped out of school as teenagers and younger adults. Chief among these were the influences of parents, siblings, and peers both in and out of school. This article considers these research findings, in light of the educational literature, as a catalyst for recommending ways that high school administrators, counselors, and teachers can (1) make students' families and out-of-school friends feel comfortable with the school setting, (2) teach students' parents and guardians how to support their children's educational efforts, and (3) teach students how to engage in positive interactions with peers. Introduction A 2003 qualitative case study examined the perspectives of 70 stakeholders connected to two community-based adult literacy programs in rural and northern Manitoba Canada The research purpose was to develop an understanding of regular school dropouts' participation in these programs. Within the context of describing their learning experiences, the 37 learners in the study revealed a variety of reasons for having dropped out of regular school as teenagers and younger adults. Chief among these were the influences of parents, siblings, and peers both in and out of school. This article considers these research findings, in tight of the educational literature, as a catalyst for recommending that high school administrators, counselors, and teachers pay particular attention to the relationships that students-at-risk have with family members and peers. There is little in the research literature that records the story of dropping out from the retrospective of students who have taken this journey and are now seeking a second chance to improve their academic skills. This original research report thus adds a critical dimension to the existing literature. All given names are pseudonyms. The following definitions of terms apply, in accordance with their use by program stakeholders: adult learners are adult literacy students, regular schools are private or public grade schools, students-at-risk are regular school students who are at risk of dropping out, and youth-at-risk are individuals under the age of 21 years who have already quit or are at risk of doing so. Research Methodology The two research programs were selected from 37 adult literacy programs that received provincial funding in 2002-03. Although both programs were obligated to follow the community-based program model endorsed by the Government of Manitoba they had somewhat different foci for program delivery. One program offered adult high school courses as well as basic literacy. It had a reputation of helping adult dropouts complete grade 12, as well as blending adult literacy and high school curricula and delivering internationally recognized Microsoft computer courses. The other program offered instruction ranging from beginning literacy to post secondary tutorial support. It had a reputation of accepting every learner who asked for help, and of successfully meeting the special needs of students with learning disabilities and other learning challenges. The 70 research participants who volunteered for the study belonged to seven program stakeholder categories, as follows: 37 learners (adult literacy students), 2 coordinators/instructors (equivalent to teaching principals in regular schools), 11 other staff members (instructors and office support staff), 7 parents and other close relatives of learners, 2 program administrators (equivalent to regular school board members), 8 referral agents (from community organizations and government agencies), and 3 provincial funding agents (civil servants responsible for government funding). Data were derived from three sources: official documents, personal documents, and one-to-one interviews. The official documents were the programs' year-end reports for 2001-02 and 2002-03, including information about program hours, teaching methods, learning resources, and finances. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,004 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».