Improving South African student teachers’ English language skills: an argument for the assessment strategies of the PrimTEd language teaching project
Bibliographic record
Abstract
South Africa is a linguistically diverse and educationally complex country. Most student teachers in Bachelor of Education programmes who are preparing to teach in primary schools do not speak English as a mother tongue. The medium of instruction for B.Ed programmes is English. Foundation Phase teachers will be expected to teach learners English (as a) First Additional language (EFAL). Intermediate Phase teachers will be expected to use English across the curriculum as English is the Language of Learning and Teaching (LoLT) from grade 4 upwards. As such, it is important that newly qualified teachers entering primary schools can engage with English texts, have a competent understanding of English and communicate fluently in English. As one way of making a positive intervention in future teacher competency, this paper argues for the use of language and literacies assessment in Initial Teacher Education (ITE)at universities. The Primary Teacher Education project (PrimTEd) has developed a set of Language and Literacy standards for teachers, as well as assessments for primary school student teachers’ knowledge of English. These assessments are designed to occur at two points: entry level (first year) and exit level (fourth year) of the Bachelor of Education (B.Ed) degree. Methodologically this paper considers the complex background conditions in language education which led to the PrimTEd project’s work and then sets out how the PrimTEd project’s assessment strategy may offer a hopeful intervention in these circumstances.
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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 | 0.077 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| 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".