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Record W1945155274 · doi:10.5539/elt.v8n6p1

Word Difficulty and Learning among Native Arabic Learners of EFL

2015· article· en· W1945155274 on OpenAlexvenueno aff
Ahmed Masrai, James Milton

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyVocabularyLinguisticsRepetition (rhetorical device)Equivalence (formal languages)Vocabulary developmentWord (group theory)Word lists by frequencyFirst language

Abstract

fetched live from OpenAlex

This study investigates word difficulty and learning among learners of English as a foreign language (EFL) in Saudi Arabia. Difficulty factors examined in the study include repetition of words in learners’ EFL textbooks, word length and parts of speech, and adds a further consideration which is underexplored in the literature; word translation equivalents in the learners’ first language (L1). A total of 156 native Arabic participants were given a vocabulary test in which they had to identify whether a word was known to them and then to supply the meaning of the word in their L1 or L2. The findings showed a large effect of repetition on word learnability, accounting for 60% of the variance, followed by translation equivalence, which explained some 23% of the variance. Conversely, word length and the parts of speech element provided non-significant contributions to the overall model of learning. Thus, the results indicate a durable effect of repetition and a modest influence of L1 translation equivalent on the L2 vocabulary learning, regardless of the number of syllables in a word or the part of speech element.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.296
Teacher spread0.281 · 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 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

Citations9
Published2015
Admission routes1
Has abstractyes

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