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Record W2789064076 · doi:10.5539/ijel.v8n3p55

The Impact of Structured Input and Consciousness Raising Tasks on the Acquisition of Implicit and Explicit Knowledge of EFL Learners

2018· article· en· W2789064076 on OpenAlexvenueno aff
Forouzan Zereshki, Ghafour Rezaie

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsGrammaticalityExplicit knowledgeImplicit knowledgeRaising (metalworking)ConsciousnessPsychologyImplicit learningCognitive psychologyConsciousness raisingTest (biology)Second-language acquisitionCognitionLinguisticsComputer scienceCognitive scienceArtificial intelligenceGrammarPedagogyMathematics

Abstract

fetched live from OpenAlex

During the past decades, the distinction between implicit and explicit knowledge and how they could be developed through instruction have always been controversial issues for cognitive psychologists and second language acquisition (SLA) researchers. The present study was aimed at investigating the effects of two different input-based tasks (Structured Input and Consciousness Raising) on the acquisition of implicit and explicit knowledge of English active causative structure by EFL learners. Seventy three female English language learners participated in this study. Participants were divided into two experimental groups, one was provided with structured input activities and the other with consciousness raising activities. The participants’ implicit and explicit knowledge of the target structure was assessed through Timed Grammaticality Judgment and Untimed Grammaticality Judgment respectively before and after the treatment. The results of Paired and Independent Samples t-test analyses revealed that both C-R tasks and SI tasks resulted in the acquisition of both implicit and explicit knowledge, with C-R having more significant impact on the explicit knowledge. The findings provided indirect positive support for the interface hypothesis.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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 head0.036
GPT teacher head0.405
Teacher spread0.369 · 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 designObservational
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

Citations2
Published2018
Admission routes1
Has abstractyes

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