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Record W2586087559 · doi:10.5539/elt.v10n3p63

Chinese Secondary EFL Learners’ and Teachers’ Preferences for Types of Written Corrective Feedback

2017· article· en· W2586087559 on OpenAlexvenueno aff
LI Hai-shan, Qingshun He

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersGuangdong University of Foreign StudiesChina Scholarship CouncilUniversity of Queensland
KeywordsCorrective feedbackPreferencePsychologyMathematics educationTeaching methodPedagogyMathematics

Abstract

fetched live from OpenAlex

How learners perceive written corrective feedback (CF) associates with its effectiveness in language learning. This research investigates students’ preferences for three types of written CF, i.e., direct, indirect and metalinguistic written CF, and explores the factors that encourage the teachers to employ these CFs in teaching practice. The findings include: (1) indirect written CF is preferred by most Chinese secondary EFL learners and there exist significant differences among their preferences, (2) gender difference significantly influences learners’ preference for metalinguistic written CF and proficiency differences significantly influence their preference for indirect written CF, (3) indirect written CF is most commonly used by the teachers of secondary levels and (4) there are no significant differences between learners’ preferences and teachers’ practice in Chinese secondary schools. This research thus presents a new acquaintance with learners’ preferences and teachers’ justification for their execution.

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)0.002
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 score0.149
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.279
Teacher spread0.260 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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