Symbolic Representations as Teachers Reflect on Inclusive Education in South Africa
Bibliographic record
Abstract
The inclusive education movement generated many changes in the education system worldwide, resulting in teachers needing to change their practice and beliefs and implement inclusive teaching strategies to accommodate a more diverse learner population. Numerous professional development courses were conducted in South Africa, however the expected change in attitude and practice proved difficult to achieve for many. Even after attending such courses, teachers found it challenging to establish inclusive learning environments, believing they did not have the necessary skills and resources to teach learners with divergent learning needs. This study explored the pivotal role of teachers and their personal perspectives of themselves as inclusive practitioners in South Africa after attending a professional development course on inclusive education strategies. A qualitative, interpretative research design was utilised whereby visual symbols alongside written reflections were analysed to identify changes in teachers’ knowledge, beliefs and practice. Findings revealed that teachers’ knowledge had increased, their attitudes towards learners with diverse needs was more positive, they felt more confident in their own abilities and more equipped for the task. Critical reflection emerged as an essential skill for teachers to be able to question their beliefs and rethink their practice but that this skill needed to be actively taught and encouraged in order to change prevailing perceptions of diversity and improve teaching practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".