Exploring Instructional Strategies in an Indigenous Education Course in Initial Teacher Education
Bibliographic record
Abstract
This paper explores the different approaches and instructional strategies of five instructors who have taught a mandatory Aboriginal Education course in a Canadian teacher education program. The findings are drawn from a broader study that examined the impact of self-reflective practices in Indigenous Education. Through semi-structured interviews, five course instructors shared what they believed to be meaningful instructional strategies in the course. Although each instructor shared their personal approach to the course, the four broad themes of story, land, art, and reflection emerged from their examples. These are discussed in relation to what is reported in the literature about the approaches and instructional strategies of Indigenous Education course instructors (Aveling, 2006; Dion, 2007; McInnes, 2017); this expands the conversation on meaningful pedagogies for Indigenous Education courses in initial teacher education. Keywords: Indigenous Education; teacher education; instructional strategies; pedagogical practices. Cet article explore les différentes approches et stratégies pédagogiques de cinq instructeurs qui ont enseigné un cours obligatoire d’éducation autochtone dans le cadre d’un programme de formation des enseignants au Canada. Les résultats sont tirés d’une étude plus vaste ayant porté sur l’impact des pratiques de réflexion personnelle en éducation autochtone. Cinq instructeurs ont partagé, par des entrevues semi-structurées, ce qu’ils croyaient être des stratégies pédagogiques significatives pour le cours. Même si chaque instructeur a partagé son approche personnelle au cours, quatre grands thèmes, soit le récit, le territoire, l’art et la réflexion, sont tout de même ressortis de leurs exemples. Ces thèmes font l’objet d’une discussion portant sur les approches et les stratégies pédagogiques des instructeurs d’éducation autochtone qui sont décrites dans la littérature (Aveling, 2006; Dion, 2007; McInnes, 2017). Cette discussion vient donc élargir la conversation sur les pédagogies significatives pour les cours d’éducation autochtone offerts dans le cadre de la formation des enseignants. Mots clés : éducation autochtone; formation des enseignants; stratégies pédagogiques; pratiques pédagogiques
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| 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".