Making the grade: teacher training for inclusive education: A systematic review
Bibliographic record
Abstract
The role of teacher training, as it pertains to the adoption of inclusive education (IE) ( European Journal of Special, 22 , 2007, 367), is critical in realizing and achieving truly IE environments. Literature often reports poor or inadequate training with regard to IE practices ( Preparing Teachers of the Deaf for a Complex, Jacksonville, FL). The purpose of this review was to examine North American and Australian research regarding teacher training for students with disabilities (SWD) with the goal of informing best practice. Of the 27 reviewed studies, teacher training interventions reported positive outcomes and showed improvements in the areas of teachers’ attitudes/perceptions, knowledge, and strategies/skill development ( Teacher Education and Special Education: The Journal of the Teacher Education Division of the Council for Exceptional Children , 32, 2009, 166) regarding IE SWD. The researchers cautiously recommend the employment of workshop style approaches as they appear to have the capacity to tackle all three outcome variables concurrently and within the shortest timeframe. Our restrained recommendations for best practice are born out of the quality of evidence presented within the reviewed studies.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".