Decolonizing Assessment Practices in Teacher Education
Bibliographic record
Abstract
Ongoing discoveries of mass graves at the former sites of Indian Residential Schools are tragic reminders that the work of advancing truth and reconciliation in Canada must continue. Although the imperative is clear for teacher educators to include Indigenous perspectives in classrooms, many complexities exist that stem from the legacy of colonization. Postsecondary structures and pedagogies are dominated by Western epistemology, and steps must be taken to avoid incorporating Indigenous ways of knowing and coming to know into assimilative frameworks. This chapter presents a collaborative action research design used to work towards decolonizing assessment practices in a fully online teacher-education course. The design drew upon decolonizing principles of storytelling and negotiation to inform shifts in the learning tasks, the formative assessment, and the determination of grades. The challenges encountered and the pedagogical decisions made are discussed with the aim of inviting readers into an ongoing journey of seeking to decolonize assessment practices. The authors are Indigenous and non-Indigenous instructors in a Bachelor of Education after-degree program located in the traditional territories of the Blackfoot Confederacy, the Tsuut’ina Nation, and the Stoney Nakoda Nations.
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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 | 0.015 | 0.019 |
| 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 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".