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Record W1498884530

A Study on the Effect of Scaffolding through Joint Construction Tasks on the Writing Composition of EFL Learners

2011· article· en· W1498884530 on OpenAlexvenueno aff
A. Majid Hayati, Zohreh Ziyaeimehr

Bibliographic record

VenueStudies in literature and language · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSignificant differenceMathematics educationTask (project management)Composition (language)PsychologyShahidJoint (building)Reading (process)Computer scienceLinguisticsMathematicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

The present study is an attempt to investigate the effect of scaffolding writing proficiency through joint construction tasks on the writing composition of Iranian EFL learners and to investigate any significant difference in the writing proficiency of the girls and boys after receiving the instruction. To this end, sixty intermediate learners of English, majoring in Literature and Translation, studying at Shahid Chamran University of Ahvaz participated in the research and then were randomly divided into two groups, the experimental and the comparison. During the course of this study, i.e. 10 sessions, the participants were assigned to write compositions of about 150 words on eight writing topics. To find out whether there is any significant difference in the writing proficiency of the learners who receive join construction instruction, two tests were used to compare the writing performances of the groups: a pretest prior and a posttest. Results of the Data analysis indicated that there is a significant difference in the writing proficiency of the learners who receive join construction instruction. The results also showed that, as far as the instruction on joint construction was concerned, females outperformed the males. Key words: Scaffolding; Joint Construction Task; Composition; Writing

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.239

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.303
Teacher spread0.245 · 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

Citations5
Published2011
Admission routes1
Has abstractyes

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