Supporting Kindergarten Student’s Development of Knowledge, Skills and Understanding about Truth and Reconciliation
Bibliographic record
Abstract
In Kindergarten, educators and students are called to action to engage in learning related to truth and reconciliation. The purpose of this study is to explore how educators can engage Kindergarten students in learning about truth and reconciliation within three Kindergarten classrooms. Using a qualitative case study approach, this study investigates the question: What practices support Kindergarten student’s development of knowledge, skills and understanding about truth and reconciliation? This study will extend our understanding of how to respond to the Calls to Action in the early years theoretically and pedagogically and contribute to the literature.The results from this study indicate that there are practices that educators can engage in as they move through a continual process of teaching and learning about truth and reconciliation and adopt a ‘reconciliatory stance’ to support Kindergarten students’ knowledge, skills and understanding of truth and reconciliation. Challenges related to teaching and learning about truth and reconciliation in Kindergarten include encountering resistance or pushback from others regarding the appropriateness of this work and the lack of capacity building for educators. The seven major findings from this study include: 1) there is a continual process that educators must engage in throughout the school year to develop knowledge, skills and understanding about truth and reconciliation; 2) there are practices that educators can use to support the development of knowledge, skills and understanding about truth and reconciliation; 3) educators need to have a strong understanding of how to connect teaching and learning about truth and reconciliation to the Ontario Kindergarten Program document and other guiding documents; 4) educators must continue to engage in professional learning to have the knowledge and understanding of how to bring this work into their practice; 5) more Kindergarten-specific resources need to be developed; 6) adopting a ‘reconciliatory stance’ allows educators to respond to teachable moments within the classroom related to teaching and learning about truth and reconciliation; and 7) this work needs to be done in relation and in community. Implications of this study suggest that there are powerful possibilities for developing a strong foundation for truth and reconciliation with students in Kindergarten.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".