“Maybe We Have to Create Something Different”: Fostering Inclusion in Montessori Education
Bibliographic record
Abstract
How Does Learning Happen: Ontario’s Pedagogy for the Early Years, the early childhood education framework for Ontario (Canada), aims to guide early-years programs across the province by recognizing children as competent, capable, and curious individuals from diverse backgrounds. The policy highlights the significance of ensuring inclusive learning environments that foster a sense of belonging and enable every child to flourish (Ontario Ministry of Education, 2014). Many Montessori schools across the province share this view (Hunt et al., 2022) and strive for inclusive programs that meet the learning goals of children with special education needs; however, at times, this objective can seem daunting. In this article, we highlight findings from a study involving the educators at one Montessori school focusing on the self-described goal of improving the quality of their inclusive practices through an examination of beliefs and a continuous professional learning process. The main themes identified in the study related to educators’ attitudes to inclusion and their beliefs about how the Montessori method challenges inclusion pedagogies. Moreover, we found that educators’ understanding and implementation of differentiated instruction (Tomlinson & Imbeau, 2023) was lacking. The results indicate that Montessori educators’ inclusive practices and learning environments benefited from participating in ongoing, scaffolded professional learning specifically targeted to their needs and context.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".