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Record W4292348709 · doi:10.20360/langandlit29612

ESL Writing Instruction in K-12 Settings: Pedagogical Approaches and Classroom Techniques

2022· article· en· W4292348709 on OpenAlexaffvenue
Subrata Bhowmik, Marcia Kim

Bibliographic record

VenueLanguage and Literacy · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLiteracyMathematics educationPedagogyComputer sciencePsychology

Abstract

fetched live from OpenAlex

Writing is an important literacy skill that K-12 students must develop for academic success. For young ESL students, developing writing skills entails both learning English and developing writing as a literacy skill. The need for this dual skill development underlines the challenges of teaching K-12 ESL writing, as teachers must strike a balance between teaching writing as a tool for students’ English language development and literacy skill. This paper reports on findings related to pedagogical approaches and classroom techniques that are prevalent in K-12 ESL writing instruction. Our research is based on a systematic review of 49 studies published between 2010 and 2019. Using content analysis, three pedagogical approaches were identified: (a) approaches centered on teacher perspectives, (b) approaches centered on student perspectives, and (c) approaches centered on emerging research and theories of ESL writing instruction. As well, the analysis yielded four classroom techniques: (a) adopting SFL-oriented and genre-based activities, (b) utilizing ESL-bilingual student writers’ language learning traits, (c) incorporating digital technology, and (d) adapting instructional practices in response to student needs. Critically reflecting on these pedagogical approaches and classroom techniques, the paper discusses the advantages and challenges of implementing them in the classroom. The paper provides a taxonomy of instructional practices that K-12 ESL writing teachers may find useful.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.445
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.272
Teacher spread0.234 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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