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Record W4281775972 · doi:10.1111/flan.12632

L2 listening: An intervention study of instructional approaches

2022· article· en· W4281775972 on OpenAlexaff
Jesús Toapanta

Bibliographic record

VenueForeign Language Annals · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsActive listeningPsychologyVocabularyIntervention (counseling)Session (web analytics)MetacognitionInformational listeningMathematics educationPedagogyListening comprehensionLinguisticsCognitionPsychotherapistComputer science

Abstract

fetched live from OpenAlex

Abstract This intervention study was conducted to assess the effects of three instructional approaches to L2 listening. That is, a metacognitive pedagogical cycle, an awareness‐raising approach which combined reflections and short discussions of factors associated with successful L2 listening, and an approach that incorporated vocabulary as a prelistening activity and guiding questions during the listening session. This study embraces the need for intervention studies that identify what works best. It addresses methodological flaws in previous studies and concerns associated with long‐term effects. L2 listening was measured before and after the intervention at pretest, posttest, and delayed posttest. The overarching research question was concerned with whether or not there are significant differences in L2 listening between and within the groups. The results indicate that guiding learners through a process that develops metacognitive knowledge and regulatory skills is an effective way of teaching listening. The results also offer preliminary evidence of long‐term effects.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.124
GPT teacher head0.306
Teacher spread0.182 · 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 designNon-randomized trial
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

Citations4
Published2022
Admission routes1
Has abstractyes

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