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Record W2021383304 · doi:10.5430/elr.v1n2p102

The Innovated Writing Process (IWP) Approach: a Practical Bridge between Recent SLA and Applied Linguistics Theories

2012· article· en· W2021383304 on OpenAlexvenueno aff
Anwar Mourssi

Bibliographic record

VenueEnglish Linguistics Research · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSecond-language acquisitionLinguisticsComputer scienceProcess (computing)Bridge (graph theory)Applied linguisticsContext (archaeology)Sociocultural evolutionCorrective feedbackMathematics educationPsychologySociologyProgramming languagePhilosophy

Abstract

fetched live from OpenAlex

Based on the shortcomings found in previous methods of teaching writing and following recent works in applied linguistics and second language acquisition on form-focused instruction, explicit teaching and learning, and types of feedback, the Innovated Writing Process (IWP) approach was designed. This is one of the findings of an empirical study in the context of Arab learners of English (ALEs) in the Sultanate of Oman. It is also an attempt to apply Sociocultural Theory in classroom settings and to show how input can be well-processed which, in turn, can develop the second language (L2) learners’ internalized grammatical system.The method used in this study indicates that metalinguistic feedback- one form of interaction between the instructor and learners - may be one of the most successful feedback types in helping L2 learners acquire second language linguistic items.

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)0.011
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.021
Scholarly communication0.0080.011
Open science0.0030.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.190
GPT teacher head0.413
Teacher spread0.223 · 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 designTheoretical or conceptual
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

Citations5
Published2012
Admission routes1
Has abstractyes

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