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Record W7116977080 · doi:10.5287/ora-2zqr4eb7e

Evaluating approaches to EAL newcomer support: protocol for a systematic review.

2021· dissertation· en· W7116977080 on OpenAlexaboutno aff
Vincent Murphy

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamVariety (cybernetics)Inclusion (mineral)TrustworthinessProtocol (science)Qualitative researchMainstreamingNarrative

Abstract

fetched live from OpenAlex

Background There has been an influx of students in the United Kingdom, Australia, Canada, and Ireland who arrive in the country without adequate English skills to fully participate in school. Governments and schools utilised a variety of methods to cater to these newcomer students. Across the aforementioned countries, numerous different approaches to English as an Additional Language (EAL) provision. These can be best categorised as placing newcomers in the mainstream classes (mainstreaming model) or withdrawing them for separate, isolated provision (withdrawal model). Research is lacking on the relative effectiveness of these two different models of newcomer EAL provision. Objectives This systematic review seeks to uncover the nature and extent of research on newcomer EAL provision in the United Kingdom, Australia, Canada, and Ireland. It aims to provide commentary on the relative effectiveness of different approaches and to determine whether policies aimed at newcomers are supported by solid evidence. Methods This review uses a best-evidence, systematic review approach to answer the research questions. The search string identified 3,332 records. Using pre-defined inclusion and exclusion criteria the studies were screened for suitability. I evaluated the trustworthiness of each study based on study design and reporting practices. Results The review found 15 eligible studies, with six in each of Australia and the United Kingdom, two in Ireland and a solitary study conducted in Canada. Qualitative data was reported in each study, so a narrative synthesis was used. Researchers primarily conducted ethnographic studies to investigate how mainstreaming and withdrawal impacted newcomers and their emotional, social, and academic development. No records had a study design that was able to directly assess effectiveness of approaches, with comparisons between the two models non-existent. From observations and interviews with EAL stakeholders, researchers reported positive and negative aspects of both mainstreaming and withdrawal approaches. Mainstreaming was predominantly found to be exclusionary and academically unhelpful for newcomer students. Conversely, withdrawal approaches offered some tangible benefits for newly arrived students. Implications For researchers, this study shed a light on the lack of solid evidence which had empirically assessed the efficacy of approaches to newcomer EAL provision. Further research is desperately needed in this field as the current state of the field has resulted in nebulous, fragmentary, and inconsistent. With regards to policy, governments have not listened to the little evidence that does exist and instead have made policies in reaction to social movements that have little evidence to support them. The implications for policy makers and practitioners are therefore clear: re-evaluate current approaches to EAL provision and follow research suggestions as to the most effective model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.156
metaresearch head score (Gemma)0.185
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.156
Threshold uncertainty score0.827

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1560.185
Meta-epidemiology (narrow)0.0060.007
Meta-epidemiology (broad)0.0150.016
Bibliometrics0.0160.018
Science and technology studies0.0050.006
Scholarly communication0.0100.012
Open science0.0060.007
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0840.015

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.

Opus teacher head0.304
GPT teacher head0.488
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreProtocol

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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