A collaborative approach to enhance quality education in Foundation Phase inclusive classes in South Africa
Bibliographic record
Abstract
Teachers in South African schools have the mandate to work together with parents to implement the inclusive education policy. However, schools and families continue to work as separate entities, and this negatively affects the support provided to learners experiencing barriers to learning. Insufficient collaboration between teachers and parents has hampered the ability to support learners who experience barriers to learning. Barriers to learning must be identified, and learners must receive collective support as early as in the Foundation Phase so that barriers do not continue to affect learners’ learning. Notably, there is minimal support for such learners in disadvantaged schools due to limited collaboration among support stakeholders. This study investigated how a collaborative approach can be useful to enhance effectiveness in teaching Foundation Phase inclusive classes. The study was underpinned by Ubuntu theory. Purposive sampling was used to select participants, and qualitative research methodology based on an interpretive paradigm was used. Focus group interviews, involving the recording of the discussion, were used to generate data. The findings of the thematic analysis revealed the importance of collaboration among teachers, parents, and other stakeholders to achieve and enhance effective inclusive teaching in the Foundation Phase. We conclude that locally available assets that reside in parents within the school environment should be used to promote inclusive teaching and learning.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 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".