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Record W4403779247 · doi:10.5539/elt.v17n11p92

An Action Research on the Effects of “Production-Oriented Approach” on Senior High School Students’ English Writing

2024· article· en· W4403779247 on OpenAlexvenueno aff
Jia‐Li Du, Baiyinna Wu, Yi Cao

Bibliographic record

VenueEnglish Language Teaching · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAction researchMathematics educationAction (physics)Production (economics)Pedagogy

Abstract

fetched live from OpenAlex

The present study explored the effects of “production-oriented approach” (POA) on senior high school students’ English writing. 8-week action research of English writing in a senior high school in Inner Mongolia, China was carried out. The data were collected through writing tests and the questionnaire to answer the following research questions: What are the effects of POA on the senior high school students’ English writing performance and their critical thinking skills (CTSs)? The research results show that: 1. teaching writing based on POA could improve the language and content of English writing. In specific, concerning language, POA could improve the syntactic complexity, lexical diversity, lexical variation of complexity, fluency, and accuracy of English writing. However, there was no significant effect on the structure of English writing. 2. POA positively influenced students’ CTSs, especially in the dimensions of analysis, curiosity, justice, cognitive maturity, open-mindedness, and tenacity. However, the writing teaching with POA did not influence the dimensions of truth-seeking and self-confidence. Finally, combined with the research findings, some suggestions for improving senior high school students’ English writing and CTSs were put forward, and the limitations of this research and directions for the future research were discussed.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.389
Teacher spread0.356 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes1
Has abstractyes

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