Teacher Education: Prospective Teachers’ Expectations of Addressing Indigenous Students’ Identities
Bibliographic record
Abstract
School board and school administrators, as well as classroom teachers, are invited to re-examine the complex socio-historical outcomes that have had an adverse effect upon Indigenous student engagement and achievement in public schools. Recent policy initiatives in Ontario have focused upon improving the educational experiences of Indigenous students in publicly-funded schools. To better inform policy discussions and the relevant literature, this study examines the perceptions of teacher candidates prior to their teaching assignments in K to 12 public schools in southwestern Ontario. It investigates prospective teachers’ expectations of their professional teacher education program in terms of preparing them to address Indigenous students’ diverse learning needs, and their own awareness of issues related to identity and social justice as reflected in the goals of the 2007 Policy Framework and the other respective Ontario Ministry of Education documents. The mixed-methods study is in response to a void in the research that too often has not considered preservice teacher perceptions of the relationship between their learning, the professional program of study, and their actual experiences in the classroom as student-teachers. The findings of the study include participants’ expectation that there will be significant differences between Indigenous and non-Indigenous students’ learning needs and preferences, and that issues of diversity will implicate significantly on their practice. Moreover, prospective teachers expect to be directly supported in facilitating culturally-responsive classrooms.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".