Affordances and limitations of a special school practicum as a means to prepare pre-service teachers for inclusive education
Bibliographic record
Abstract
As education systems worldwide embrace inclusive education in some form, pre-service teachers need to be prepared to be pedagogically responsive to diverse students and learning needs. While much learning for inclusion takes place in course work in higher education institutions, field experiences, including practicum placements, can complement this learning. Using Loreman's [2010a. “Essential Inclusive Education-Related Outcomes for Alberta Preservice Teachers.” The Alberta Journal of Educational Research 56 (2): 124–142] seven areas of essential learning for inclusion, with the addition of Waitoller and Kozleski's [2010. “Inclusive Professional Learning Schools.” In Teacher Education for Inclusion, edited by C. Forlin, 65–73. London: Routledge] idea of ‘critical sensibilities’, this article considers the extent to which a practicum experience in a special school might contribute to learning for inclusion. The main findings of a small-scale qualitative study with 15 South African pre-service teachers suggest that the practicum placement exposes them to children with disabilities and learning difficulties, resulting in a growth of understanding of their learning needs. It also enhances pre-service teachers' ability to plan lessons and draw on a range of instructional strategies to enable learning for all. For some pre-service teachers, however, the practicum convinced them of the benefits of separate special education and the unfeasibility of inclusion. We conclude that a special school practicum has value for pre-service teachers, provided that opportunities are made available for critical engagement with the potential for both inclusion and exclusion of students with special educational needs in different types of 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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".