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Record W2028145550 · doi:10.5539/elt.v5n5p129

Studies and Suggestions on English Vocabulary Teaching and Learning

2012· article· en· W2028145550 on OpenAlexvenueno aff
Shigao Zheng

Bibliographic record

VenueEnglish Language Teaching · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyPsychologyVocabulary learningCompetence (human resources)Teaching methodCognitive styleLinguistic competenceMathematics educationVocabulary developmentCognitionLanguage acquisitionClass (philosophy)Learner autonomyAutonomyTask (project management)Language educationLinguisticsComputer scienceComprehension approachArtificial intelligence

Abstract

fetched live from OpenAlex

To improve vocabulary learning and teaching in ELT settings, two questionnaires are designed and directed to more than 100 students and teachers in one of China’s key universities. The findings suggest that an enhanced awareness of cultural difference, metaphorical competence, and learners’ autonomy in vocabulary acquisition will effectively facilitate the vocabulary learning. Simultaneously, a teaching model is recommended based on the findings of this survey, which incorporates ideas advocated by cognitive linguistics. Class instruction on vocabulary learning strategies can help students gain awareness of learning strategies. The greater the strategy awareness of learners, the more likely they will be to use task-appropriate learning strategies that help them overcome their general learning style limitations, and the more likely that these strategies will assist in processing, retrieving, and using new language information.

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)0.011
metaresearch head score (Gemma)0.022
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0020.002
Scholarly communication0.0030.005
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.023
GPT teacher head0.278
Teacher spread0.255 · 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

Citations22
Published2012
Admission routes1
Has abstractyes

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