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Record W4295927907 · doi:10.1017/s0272263122000316

Explicit Instruction within a Task: Before, During, or After?

2022· article· en· W4295927907 on OpenAlexaff
Gabriel Michaud, Ahlem Ammar

Bibliographic record

VenueStudies in Second Language Acquisition · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité de MontréalHôtel-Dieu de Montréal
Fundersnot available
KeywordsGrammaticalityTask (project management)ImitationExplicit knowledgePsychologyTest (biology)Control (management)Cognitive psychologyComputer scienceSocial psychologyLinguisticsArtificial intelligenceGrammar

Abstract

fetched live from OpenAlex

Abstract This study addresses the effects of the timing of explicit instruction within the three phases of a task cycle (pretask, task, posttask) while considering learner’s previous knowledge. Eight intact groups ( N = 165) of French L2 university-level students (4 B1- and 4 B2-level groups) completed two tasks. Groups were formed according to previous knowledge. Three groups received explicit instruction on the French subjunctive during the pretask, task, or posttask phase of each task. The control groups completed the task without prior instruction. Participants completed an elicited imitation test and a grammaticality judgment test as pretests, immediate posttests, and delayed posttests. Results showed that explicit instruction embedded in a task facilitates the development of explicit and implicit knowledge and that the efficacy of instruction is not significantly influenced by the timing at which it is provided or by the learners’ level of previous knowledge.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.279
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations18
Published2022
Admission routes1
Has abstractyes

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Same venueStudies in Second Language AcquisitionSame topicEFL/ESL Teaching and LearningFrench-language works237,207