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Record W1999729806 · doi:10.5539/ass.v8n3p88

The Effect of Process Writing Practice on the Writing Quality of Form One Students: A Case Study

2012· article· en· W1999729806 on OpenAlexvenueno aff
Majid Pourmohammadi, Mohamad Jafre Zainol Abidin, Cheong Lai Fong

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPaceWriting processProfessional writingQuality (philosophy)Task (project management)Process (computing)Second language writingMathematics educationPsychologyScientific writingComputer sciencePedagogyLinguisticsSecond language

Abstract

fetched live from OpenAlex

Teachers who teach English as a second language are committed to develop an understanding and improvement of second language writing as learning to write in the second language is a complicated process. Despite the numerous research on second language writing, the phenomenon of writing is constantly under investigation as teachers want to share the findings of each study which provides knowledge that resonates with their teaching experience. This case study is a report about the effect of process writing practice on the writing quality of three Form one students. This study uses observation, interview, students’ drafts, and writing and examination scores as sources for data collection. The analyses show some effects on their writing pace and writing quality. The findings also reveal that students are able to attempt writing despite the difficulty of the given writing task.

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)0.007
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.041
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
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 head0.073
GPT teacher head0.423
Teacher spread0.350 · 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 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

Citations32
Published2012
Admission routes1
Has abstractyes

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