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Record W4392343135 · doi:10.18806/tesl.v40i1/1387

Teaching Elementary ESL Writing in Canada

2024· article· en· W4392343135 on OpenAlexaffvenueabout
Subrata Bhowmik, Marcia Kim

Bibliographic record

VenueTESL Canada Journal · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMathematics educationPedagogyPsychologyLinguisticsLanguage educationLanguage assessmentSheltered instructionTeaching methodSociologyComprehension approachPhilosophy

Abstract

fetched live from OpenAlex

Writing is an important skill for children’s academic success (e.g., Fitts et al., 2016), underlining the need for effective ESL writing instruction in the elementary classroom (Brisk, 2012; Mohr, 2017; O’Hallaron, 2014). However, there is a paucity of research on elementary ESL writing instruction in Canada. Specifically, we have little understanding about the pedagogical practices in this context. To fill this gap, this paper reports on findings of a study that investigated: (a) factors that influence teacher preparedness, and (b) challenges teachers encounter in teaching ESL writing. Eight elementary teachers, each with at least three years of teaching experience, participated in the study. Data were collected from interviews and online surveys. Findings suggest that teacher preparedness was affected by four factors: (a) background knowledge of teaching ESL writing, (b) professional learning opportunities, (c) self-learning and experience as a teacher, and (d) collaboration, mentorship, and support for teachers. The challenges teachers encountered were grouped into five categories: (a) making sense of the writing curriculum, (b) finding relevant resources, (c) lack of time, (d) difficulty providing feedback, and (e) parental involvement at home. Drawing on these findings, the paper discusses implications and recommendations for ESL writing instruction in the elementary classroom.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.002
Insufficient payload (model declined to judge)0.0210.000

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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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 designNot applicable
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
Published2024
Admission routes3
Has abstractyes

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