Languages of learning and teaching in multilingual classrooms: educational use of the African languages
Bibliographic record
Abstract
Black South African learners are registered in Model C schools to receive their education. The language of learning and teaching is English, whilst these learners’ English language proficiency is limited. They come from different urban, townships and rural areas, and their home languages are indigenous languages. The study aims to investigate and describe the challenges facing black, English second-language South African learners and to meet their needs by offering suggestions as to how they could be assisted to learn and achieve according to their full potential. The study used qualitative analysis with interview as the main data gathering tool. The participants were teachers purposively selected from the suggested pilot schools. From the phenomenological analysis the data were gathered by means of a literature review, document analysis, interviews, classroom visits and observations. The study has revealed that the black South African learners in Model C schools are faced by numerous challenges owing to their limited English proficiency, and that they do not meet the requirements to pass their grades. Their inability to cope affects their self-esteem and confidence negatively. The learners do not take risks to participate actively during lessons as they tend to avoid embarrassment and being teased by their peers. The study resulted in formulating guidelines and recommendations that will help meet the challenges faced by black South African learners in Model C schools and support them.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".