Acquisition of Vocabulary Among Arab ESL Learners: An Empirical Analysis of Affective Factors
Bibliographic record
Abstract
This research investigates the multifaceted impact of affective factors on English vocabulary acquisition among Arab learners of English as a Second Language (ESL). A quantitative, cross-sectional research design was employed, involving 165 Arab ESL learners enrolled at a language center in Kuala Lumpur. Data were collected using a systematic questionnaire and vocabulary tests and analyzed through Structural Equation Modeling (SEM) with Smart PLS software (Version 4.0). The methodology validated constructs and tested hypothesized relationships. Results demonstrated that intrinsic motivation, self-confidence, and attitudes significantly enhance vocabulary size and depth, while anxiety had a negligible negative effect. Attitudes toward the target language showed the strongest influence, followed by intrinsic motivation and self-confidence. Together, these affective factors explained a significant variance in vocabulary acquisition. This study highlights the importance of creating supportive, culturally relevant learning environments tailored to Arab learners. By addressing affective dimensions, educators and policymakers can foster more effective vocabulary acquisition strategies. These findings contribute to theoretical advancements in second language acquisition (SLA) and offer practical insights for ESL pedagogy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".