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Record W4397008409 · doi:10.1177/13621688241248440

The differential effect of oral and written corrective feedback on learners’ explicit versus implicit knowledge

2024· article· en· W4397008409 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage Teaching Research · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of VictoriaUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsCorrective feedbackPsychologyExplicit knowledgeCognitive psychologyDifferential effectsImplicit knowledgeMathematics educationLinguisticsCognitive scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This study seeks to address a gap in our understanding of how corrective feedback (CF) influences second language (L2) learning by examining the specific impacts of oral and written CF on acquiring the third person singular -s in the simple present tense. The study examines these effects on both explicit and implicit knowledge. The research was conducted in five intermediate adult English as a second language classrooms in Peru ( N = 101), using a pretest–posttest design with one control group ( n = 24) and four experimental groups: oral recast ( n = 21) oral metalinguistic CF ( n = 18) written direct CF ( n = 16) and written metalinguistic CF ( n = 22). The results revealed no significant difference between oral and written CF; however, differences were observed based on measurement types and CF subtypes used. This study’s findings carry theoretical and pedagogical implications, contributing valuable insights to both second language writing research and pedagogy.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.060
GPT teacher head0.396
Teacher spread0.336 · 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