Merging English Home Language and First Additional Language curricula: Implications for future quality assurance practices
Bibliographic record
Abstract
South Africa has a uniquely differentiated English curriculum in a bid to cater for diverse proficiency levels prevalent among its learner population. While this stride made sense in a population with one of the highest inequalities in the world, it is equally important to reflect on whether the differentiated systems do serve the purpose of equal access in relation to the quality of provision. Surprisingly, very little research has been carried out to validate the merits of this curriculum and assessment differentiation to date. In this paper, I report on Umalusi’s commissioned study on English curriculum benchmarking with three countries: Kenya, Singapore and Canada. This four-country case analysis focuses on curriculum goals and the depth and breadth of English Home Language (EHL) and English First Additional Language (EFAL). The results of the analysis show that the objectives of EFAL and EHL are largely similar and that both compare favourably with these subjects taught in the three other countries under investigation. However, framing the study within theories of language acquisition and language variation, I argue that the EFAL/EHL differentiation at both curriculum and assessment levels is unmerited and serves the opposite intent: deepening inequalities and access to the English language. In the end, useful recommendations for repackaging an assessment of English into one that takes account of its diverse learner population are advanced and further research opportunities are highlighted.
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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 | 0.244 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".