Different Worlds Same Province: Blended Learning Design to Promote Transcultural Understanding in Teacher Education
Bibliographic record
Abstract
The history of Canada’s educational offerings for Indigenous students was based in colonial and assimilative practice. As such culturally responsive pre-service teacher education needs to respond not only by moving away from historical practice but with a moral and social imperative through programming that aids reconciliation. Current literature outlines the challenges that both Indigenous and mainstream teacher candidates have in developing efficacy towards transcultural skills development. In an effort to respond to both types of students, during the design and development of a 16-month community-based Bachelor of Education program, that was offered in parallel to a campus based program, a model of blended-education for cultural understanding was developed. Using the “elementary science methods” course as design case, this paper will outline the development of the blended model which paired campus- and community-based students. The challenges and successes of the design were determined through a thematic analysis of instructor observations of the pilot during the 2014-15 academic year. Four key themes emerged as important in fostering transcultural understandings within blended learning practice: student efficacy, relationship building, recognition of cultural bias, and legitimizing traditional ecological knowledge. Each of these will be discussed in the context of the course as well as transformative post-secondary educational experiences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".