The Effects of Service-Learning Participation on Pre-Internship Educators’ Teachers’ Sense of Efficacy
Bibliographic record
Abstract
This study aimed to determine if pre-internship teacher education students’ participation in service-learning activities in K-12 classrooms would significantly affect their teachers’ sense of efficacy (TSE). A secondary focus sought to determine if one type of service-learning activity (e.g., whole-class instruction) would affect teacher efficacy more than another (e.g., small-group tutoring). Findings revealed that pre-internship service-learners in both types of service-learning activities increased significantly in their TSE. However, neither type of service-learning activity was superior to the other as measured by the minimally accepted .05 level. The discussion focuses on factors shared between the two service-learning designs that might mediate a positive mastery experience.L’objectif de cette étude était de déterminer si la participation par des étudiants en pédagogie à des activités de bénévolat dans des classes de la maternelle à la 12e année augmenterait de façon significative leur sentiment d’être efficaces comme enseignants. Un deuxième objectif consistait à déterminer si un type d’activité de bénévolat (par ex. l’enseignement à toute la classe) affecterait l’efficacité d’un enseignant plus qu’une autre (par ex. le tutorat à de petits groupes). Les résultats indiquent que les deux types d’activité ont accru de façon significative le sentiment d’efficacité comme enseignants chez les étudiants bénévoles. Toutefois, les paramètres selon le seuil minimal de 0,05 ont révélé qu’aucune activité n’était supérieure à l’autre. La discussion porte sur des facteurs communs aux deux activités de bénévolat et qui pourraient entrainer une expérience d’apprentissage fructueuse.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".