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Record W7037460864

EFL learner and teacher perspectives on corrective feedback and their effect on second language learning motivation

2014· other· en· W7037460864 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2014
Typeother
Languageen
FieldComputer Science
TopicAdvanced Neural Network Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCorrective feedbackAffect (linguistics)Test (biology)Second languageSecond-language acquisitionSkepticismLanguage proficiencyLanguage acquisition
DOInot available

Abstract

fetched live from OpenAlex

The present mixed-methods research study examines the beliefs of 247 high school students and their 12 EFL teachers about corrective feedback in terms of its types, frequency, and their positive and negative attitudes towards it. The data were gathered by means of a questionnaire administered to all participants and in-depth interviews conducted with a subsample of 15 students and all 12 of the participating EFL teachers at two distinct research sites in Santiago, Chile: a private bilingual school and a semi-private institution. Teacher and learner perspectives on error correction were compared within and across schools in order to identify differences that might affect students' L2 motivation by quantitatively analyzing the questionnaire data by means of the Mann-Whitney U Test and by qualitatively examining the interviews through content analysis. The results revealed that in both research settings there were evident disparities between teacher and learner perspectives on corrective feedback. Whereas students expressed positive views of corrective feedback and its effectiveness as well as preferences for explicit types of correction, teachers were skeptical of its effectiveness and concerned about its effect on learners' self-confidence. Accordingly, teachers reported preferences for more implicit types of feedback. These results are discussed in terms of the potentially detrimental effects they may have on students' L2 motivation and in terms of their pedagogical implications.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.003
GPT teacher head0.156
Teacher spread0.154 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venueLibrary and Archives Canada (Government of Canada)Same topicAdvanced Neural Network ApplicationsFrench-language works237,207