Facilitating Online Learning with the 5R's: Embedding Indigenous Pedagogy into the Online Space
Bibliographic record
Abstract
This project is a collection of resources for educators and instructors within the K-12 and post-secondary systems to support the adoption of Indigenous-created frameworks in online learning environments. The discovery phase in chapter one outlines our exploration of merging two seemingly disconnected perspectives and how our own life experiences and educational background gave rise to this project. The literature review in chapter two uncovers the concepts of Indigenous Knowledge and educational technology and creates connections between the two fields, while identifying gaps in the research and the work that needs to be done. The 5R’s of Indigenous pedagogy are relationship, respect, relevance, responsibility, and reciprocity. These 5R’s serve as important reminders for course designers in K-12 and post-secondary educators and benefit all learners. Our resources and reflections address how the 5R’s can be used as best practice to enrich online teaching platforms and remote learning. The positive effect of reciprocal communication, relationship building, and embracing Indigenous-created frameworks in online learning environments extends out into the community and beyond.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".