Fostering Inclusion for Learners with Special Educational Needs through Teacher Education: Comparing Educators’ Experiences from Canada and Mauritius to Consider the Future of Inclusive Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study was to explore similarities and differences between special educator preparation in Ontario and in Mauritius through a comparative case study methodology. The cases are two practicing and experienced special educational needs (SEN) educators, one from each country, who are experts in special education teacher training programs in their respective country. Data were collected through semi-structured interviews and thematic analysis was used to analyse the qualitative data through deductive and inductive coding. Findings indicate major differences in teacher training opportunities, practicum aspects, and key challenges. On the other hand, limited technology integration and unsuccessful responses to COVID-19 disruption are similar features. Recommendations are provided including a call for increased efforts to develop and study emerging technologies to support special education training. The results of the study have implications for stakeholders and policy makers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it