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Record W4377104462 · doi:10.5430/wjel.v13n6p140

Oral Corrective Feedback and Error Analysis: Indonesian Teachers’ Beliefs to Improve Speaking Skill

2023· article· en· W4377104462 on OpenAlexvenueno aff
Karisma Erikson Tarigan, Margaret Stevani, Fiber Yun Almanda Ginting, Meikardo Samuel Prayuda, Dyan Wulan Sari, Bogor Lumbanraja

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCorrective feedbackIndonesianConversationClass (philosophy)PsychologyMathematics educationForeign languageComputer scienceLinguisticsCommunication

Abstract

fetched live from OpenAlex

This research was to investigate Indonesian teachers’ beliefs about the application of oral corrective feedback in Indonesian students’ EFL classrooms. It was limited to oral corrective feedback given for lexical, phonological, and syntactical errors in English conversation class. The participants of this research were 36 English teachers and 65 Indonesian students of English as a foreign language. This research utilized both qualitative and quantitative approaches, including the use of a close-ended questionnaire, semi-structured interview, and audio-recording to find the effectiveness of oral corrective feedback on students’ errors in speaking skills. The data analysis revealed: (1) the grammatical errors in students’ oral proficiency, (2) the most type of English teachers’ oral corrective feedback, (3) the students’ uptake in speaking skills, (4) the analysis of the use of oral corrective feedback, (5) the kinds of the students’ error based on English teachers’ experiences, (6) the students’ self-awareness of language errors, (7) the way of English teacher when delivering oral corrective feedback, (8) the students’ reasons to use oral corrective feedback based on English teachers’ beliefs, and (9) English teachers’ motivations to use oral corrective feedback. These findings suggested that English teachers should understand the students’ diverse needs, concerns, and expectations toward error correction according to their level of language proficiency.

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)0.001
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 score0.234
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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
Published2023
Admission routes1
Has abstractyes

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