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Record W4322624923 · doi:10.36534/erlj.2022.02.04

Supporting linguistically and culturally diverse English language learners by integrating first language

2023· article· en· W4322624923 on OpenAlexafffundabout
Chenkai Chi, Zhuozheng Fu, Yuhan Xiang

Bibliographic record

VenueEducational Role of Language Journal · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Windsor
FundersUniversity of MelbourneUniversity of Windsor
KeywordsClass (philosophy)The artsLanguage acquisitionEnglish languagePresentation (obstetrics)Mathematics educationPedagogyComputer sciencePsychologyArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

This qualitative study is to report an activity designed in the Learning English through the Arts program to explore the L1 use in an L2 class at a Canadian University English Language Improvement Center. The participants are five adult English language learners registered in different departments and faculties at a Canadian university. The results showed that: 1) L1 can, to some extent, facilitate students’ L2 learning; 2) home culture sharing is an effective activity when teachers consider integrating L1 in L2 class, and 3) multimodal ways of presentation are crucial in integrating L1 into L2 learning. This study can provide insights for English language teachers who want to integrate L1 into L2 classes. In addition, for teacher educators, this study can also offer suggestions for teacher education programs with an increasing need to develop competent teachers to support English language learners in a diverse learning environment./ Keywords: First Language, English language learning, English through the Arts, gamification, mobile apps

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.010
GPT teacher head0.287
Teacher spread0.277 · 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 teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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
Published2023
Admission routes3
Has abstractyes

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