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Record W4383907911 · doi:10.5430/wjel.v13n7p108

The Impact of Vocabulary Size on the Receptive Skills of Saudi EFL Learners

2023· article· en· W4383907911 on OpenAlexvenueno aff
Ahmad Alshehri

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyActive listeningReading (process)Test (biology)Test of English as a Foreign LanguageReading comprehensionPsychologyMathematics educationListening comprehensionLinguisticsEnglish languageCommunication

Abstract

fetched live from OpenAlex

The present study aimed to investigate the impact of vocabulary size on Saudi EFL learners’ reading and listening skills. Thirty-eight undergraduate Saudi EFL learners participated in the study. The study tools consisted of vocabulary size test (VST) developed by Nation and Beglar (2007). The test was developed to provide a reliable, accurate, and comprehensive measure of a learner’s vocabulary size from the 1st 1000 to the 14th 1000-word family of English word-level tests. A paid reading and listening comprehension tests were used. Both tests were obtained from ETS, TOEFL official application. The results revealed that the vocabulary size of the Saudi EFL learners in the tertiary stage was 2790 words. Furthermore, the study found a positive relationship between the scores of the vocabulary test levels and the score of the reading and listening test. This indicates that a higher level of performance in the vocabulary test may lead to a higher level of performance in the reading and listening test, and these relationships were statistically significant at the level of 0.01. Because of this, it was recommended that teachers and learners use different approaches and styles for teaching and learning vocabulary.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.254
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.316
Teacher spread0.304 · 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 designQualitative
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

Citations3
Published2023
Admission routes1
Has abstractyes

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