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Form‐Focused Instruction: Isolated or Integrated?

2008· article· en· W2104373049 on OpenAlexaff
Nina Spada, Patsy M. Lightbown

Bibliographic record

VenueTESOL Quarterly · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsConcordia UniversityUniversity of Toronto
FundersOffice of International Science and Engineering
KeywordsFluencyAutomaticityInterlanguagePsychologySecond-language acquisitionContext (archaeology)Language acquisitionMathematics educationCognitionEmpirical researchComputer scienceCognitive psychologyPedagogyLinguistics

Abstract

fetched live from OpenAlex

There is increasing consensus that form‐focused instruction helps learners in communicative or content‐based instruction to learn features of the target language that they may not acquire without guidance. The subject of this article is the role of instruction that is provided in separate (isolated) activities or within the context of communicative activities (integrated). Research suggests that both types of instruction can be beneficial, depending on the language feature to be learned, as well as characteristics of the learner and the learning conditions. For example, isolated lessons may be necessary to help learners who share the same first language (L1) overcome problems related to L1 influence on their interlanguage; integrated instruction may be best for helping learners develop the kind of fluency and automaticity that are needed for communication outside the classroom. The evidence suggests that teachers and students see the benefits of both types of instruction. Explanations for the effectiveness of each type of instruction are drawn from theoretical work in second language acquisition and cognitive psychology as well as from empirical research.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.042
GPT teacher head0.230
Teacher spread0.189 · 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 designNot applicable
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

Citations386
Published2008
Admission routes1
Has abstractyes

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