Relationship-Building in Destreamed Grade 9 Mathematics Classrooms: Exploring Best Practices and Challenges
Bibliographic record
Abstract
The purpose of this study is to explore the best practices and challenges that teachers are facing in the Ontario Grade 9 mathematics destreamed course using the lens of relationship and community building. Tranter et al.’s (2018) relationship-based approach to student well-being and achievement was used to guide this research. Using a qualitative case study approach, it explores the ways that seven secondary school teachers incorporate the eight conditions of this approach into their practices. This study highlights how these conditions are implemented by individual teachers and how it impacts their overall view of the Grade 9 destreamed course. The results from this study suggest that teachers employ many aspects of a relationship-based approach in the Ontario Grade 9 mathematics course. The major findings in this study include: community building is crucial to relationship-based education and was a priority for all teachers in this study; engagement and meaningful learning are interconnected as teachers based tasks and assessments on these two goals; teachers require more supports to properly meet the needs of students in destreamed classrooms including more time, smaller class sizes, and access to classroom-ready resources; teachers in this study used the spiral method more regularly than a unit-based or textbook organization; and destreamed courses would uniquely benefit from an intentional relationship-based approach to achievement and well-being in the classroom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| 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".