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Record W2408321301 · doi:10.5539/ies.v9n6p76

The Influence of Educational Programme on Teachers’ Error Correction Preferences in the Speaking Skill: Insights from English as a Foreign Language Context

2016· article· en· W2408321301 on OpenAlexvenueno aff
Emre Debreli, Nazife Onuk

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Mathematics educationClass (philosophy)PsychologyEnglish as a foreign languageCurriculumError detection and correctionForeign languageCorrective feedbackTeaching methodLanguage proficiencyError analysisPedagogyComputer scienceMathematics

Abstract

fetched live from OpenAlex

<p class="apa">In the area of language teaching, corrective feedback is one of the popular and hotly debated topics that have been widely explored to date. A considerable number of studies on students’ preferences of error correction and the effects of error correction approaches on student achievement do exist. Moreover, much on teachers’ preferences of error correction approaches has also been explored. However, less seems to be done with regard to teachers’ practices of error correction approaches, especially in the area of English as a Foreign Language (EFL). The present study explored EFL teacher’s preferences of error correction approaches in the speaking skill, and further focused on whether the teachers were able to employ the approaches they preferred in their classrooms. Data were collected from a group of 17 EFL teachers, through semi-structured interviews and classroom observations. The findings revealed that although the teachers had clear preferences for error correction approaches, they could not employ them in their classrooms owing to the educational programme constraints. Furthermore, it was observed that they often had to adopt approaches that they were not actually in favour of. Implications for programme and curriculum designers are further discussed.</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 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)0.000
metaresearch head score (Gemma)0.005
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 score0.161
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
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 head0.049
GPT teacher head0.409
Teacher spread0.360 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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