The ‘how long’ question: language learning trajectories of EAL learners in NSW schools
Bibliographic record
Abstract
Abstract Nearly one quarter of students in Australian schools are learning English as an additional language (EAL). However, despite international research, and one Australian study, there is limited direct evidence of the length of time required for EAL students in Australia to develop the English necessary to access the curriculum in the same ways as their non-EAL peers. This article reports on outcomes from a large-scale project that investigated this issue. The project involved analysis of NAPLAN results in reading and writing from EAL students in NSW public schools from 2014 to 2022, and was supported by NSW Department of Education. The article outlines the challenges and methodological decisions involved in undertaking research of this nature, including the use of propensity score matching to control for the possibility of confounding factors. Results from the research confirm the overall length of time required for EAL students to develop high levels of academic English; they provide evidence of students’ progress in academic writing in comparison to reading; and they show the impact of students’ different starting levels on their overall progression in academic English. The results are significant and enable conclusions to be drawn regarding the length and continuity of language and literacy support needed by EAL students; allocation of resources across schools in response to students’ needs; and the professional support needed by teachers who are working with EAL students in their mainstream classes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".