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Record W2617943327 · doi:10.3968/9487

Helping Teachers Take Control of a Course Book Facilitating Vocabulary Instruction

2017· article· en· W2617943327 on OpenAlexvenueno aff
Yuanyuan Zhu

Bibliographic record

VenueCanadian social science · 2017
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyLexisSelection (genetic algorithm)Control (management)Context (archaeology)Computer scienceCourse (navigation)Mathematics educationProcess (computing)Foreign languageVocabulary learningClass (philosophy)PsychologyVocabulary developmentTeaching methodLinguisticsPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

Although students are supposed to take control and responsibility for their own vocabulary learning, it does not necessarily mean that they study alone. More recently published EFL (English as a Foreign Language) course books have become increasingly aware of the importance of vocabulary instruction by compiling a vocabulary component in them. It facilitates vocabulary instruction by introducing a systematic and principled approach to vocabulary learning. Inevitably, there will be occasions when the selection and organization of lexis in the course book may not be appropriate in some learning context. It is thus critically important that teachers know how to process course books mentally. This paper is concerned with how course books might be evaluated and adapted with regard to the teaching of vocabulary. It is conducted by examining a sample of course books that facilitate vocabulary instruction at an intermediate level. In particular, it considers the selection criteria, organizing principles, quantity of vocabulary and methodology. For each of these, suggestion for adaption is proposed and then justified in respect of the learning processes involved and the intended outcomes.

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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.317
Teacher spread0.300 · 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 teacher head, not a consensus.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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