Scaffolding immigrant early childhood teacher education students toward the appropriation of pedagogical tools
Bibliographic record
Abstract
Teacher educators and field placement supervisors in early childhood teacher education (ECTE) programs aid their students in learning a specific repertoire of tools and skills, including pedagogical tools they can mobilize in their future practice. However, these tools reify abstract notions about how to teach young children that are consistent with the values and beliefs of the specific community of practice or culture, and culturally diverse students may ascribe different meanings and uses to the tools. This one-year ethnographic study explored how 20 immigrant and refugee students constructed understandings of the authoritative discourse during their coursework and field placements in an urban ECTE college program in western Canada. Qualitative data were collected through field notes, spatial mapping, interviews, focus groups, and artifacts/documents. Framed by sociocultural-historical theory, this paper focuses on the scaffolding methods used by teacher educators and expert peers to assist students in appropriating children’s picture books and songs as tools to use during their field placement experiences. The most effective of these scaffolding strategies used mediational devices to evoke recollections of each student’s experiences “back home,” thus advancing possibilities for more culturally resonant teacher education classes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".