MétaCan
Menu
Back to cohort
Record W2574105574 · doi:10.5539/ijel.v7n1p153

The Effect of Register Instruction on EFL Learners’ Writing Performance

2017· article· en· W2574105574 on OpenAlexvenueno aff
Mohammad Yousefi Osguee, Nader Assadi Aidinlou, Masoud Zoghi

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRegister (sociolinguistics)Task (project management)Control (management)Test (biology)PsychologyMathematics educationSignificant differenceComputer scienceSystemic functional linguisticsLinguisticsMedicineArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

The purpose of the present study was twofold. First, it examined the effect of a register-based approach to writing instruction based on the insights gained from Systemic Functional Linguistics (SFL). Second, it attempted to examine the perceptions of the participants toward the register-based approach to writing. To this end, 100 intermediate and advanced students were selected and assigned to two experimental groups (advanced and intermediate ones) and two control groups. Prior to any instruction, the participants of all groups were assigned a writing task as a pre-test. The experimental groups were treated with SFL-oriented register knowledge for 20 sessions while control groups were exposed to the traditional method of teaching writing. Following the treatment, a post test was administered to the groups. The results revealed that the participants in the experimental groups surpassed their counterparts in the control groups. The results of qualitative analysis also disclosed that learners held positive attitudes towards this approach as it heightened their interest in 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)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.039
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 science0.0010.000
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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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.

Study designNot applicable
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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicDiscourse Analysis in Language StudiesFrench-language works237,207