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Record W4295927907 · doi:10.1017/s0272263122000316

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

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

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

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.984

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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