Inclusion & inquiry-based learning: bridging the gap
Bibliographic record
Abstract
Inclusive education, practices and strategies continue to be a sought-after goal in our ever-changing early years classrooms in Canada. While there has been a considerable amount of research done to advocate for inclusion and likewise for inquiry-based instruction, the research connecting both is limited. A professional development series based on the stages of the Appreciative Inquiry (AI) methodology was offered over three sessions to the professional staff in a rural, Manitoba kindergarten to grade four school to answer the following question: How is our early years school effectively supporting all students through inquiry-based learning? The 17 staff members along with the professional development facilitator/researcher completed the Discovery, Dream, and Design stages in formal sessions and have committed to the ongoing Destiny stage of identified goals. Data was collected through participant artifacts, completed activity forms, and the researcher’s journal. Six common themes emerged from the Discovery and Dream stages: community, collaboration, student engagement, deeper learning opportunities, diverse perspectives, and the impact of physical environments. Based on these themes a set of four collective goals were established: planning for all is integral to inclusive schools, teaching practices and strategies must be supported by an inclusive learning environment, a student’s strengths and contributions are celebrated and acknowledged, and creating diverse communities which foster positive student relationships are all integral to inclusive inquiry-based classrooms. As AI is a continuing and cyclical process, the Destiny stage will continue to evolve and change with the current needs of the school.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.030 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".