Relationships that support learning: A case study analysis of Indigenous perspectives through the lens of self-regulated learning in social contexts
Bibliographic record
Abstract
This study emerged from the work of a Community of Inquiry brought together to co-construct guiding principles and practices that non-Indigenous educators might use to support Indigenous and all students, in one urban community in British Columbia, Canada. Relationships emerged as a key theme from these discussions. In the literature, relationships have been important to positive school outcomes for students. In this report of findings, we consider: (1) How did Indigenous and non-Indigenous participants in this Community of Inquiry conceptualize relationships that are important to learning? (2) From the perspective of participants, what actions could be taken to facilitate relationships in this community context? Preliminary findings suggest that participants viewed relationships which support learning as close, positive, and uplifting. Relationships were seen as extending beyond the school into multi-level social and professional relationships. Concrete suggestions for nurturing these kinds of relationships included attending community events, and engaging in discussions with others. From a self-regulated learning (SRL) in social contexts perspective, results suggest that Western schools and individuals might place a greater emphasis on socially responsible self-regulation strategies, and/or open themselves to co-regulation or socially-shared regulation with Indigenous peoples as a way of supporting students.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".