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Record W2288815747 · doi:10.5539/elt.v9n4p76

Classroom EFL Writing: The Alignment-Oriented Approach

2016· article· en· W2288815747 on OpenAlexvenueno aff
Liu Rilong

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
FundersDivision of Graduate Education
KeywordsReading (process)PsychologyTask (project management)Selection (genetic algorithm)Mathematics educationProcess (computing)Coherence (philosophical gambling strategy)PerceptionWriting processEmpirical researchComputer scienceLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

<p>This paper outlines the alignment-oriented approach in classroom EFL writing. Based on a review of the characteristics of the written language and comparison between the product-focused approach and the process-focused approach, the paper proposes a practical classroom procedure as to how to teach EFL writing. A follow-up empirical study is conducted in classroom writing assignment to illustrate the alignment-oriented approach. Results show that learners show great interest in their reading materials. Besides, learners report being greatly influenced by their reading experience in their writing process, for instance, their lexical choices, writing coherence and tense usage. In this alignment-oriented approach, several factors should be paid great attention to, such as appropriate selection of materials and relevant task perception. It is hoped that this proposed approach can provide insights and implications to other EFL teachers and promote more in-depth studies in EFL writing research.</p>

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.285
Teacher spread0.273 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations10
Published2016
Admission routes1
Has abstractyes

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