Mitigating Lexical Attrition in Saudi EFL Learners: The Role of Digital Practices and Bilingual Contexts
Bibliographic record
Abstract
This study examines lexical attrition among Saudi secondary school English as a Foreign Language learners. Lexical attrition refers to the gradual loss of previously acquired vocabulary due to factors such as code-switching, infrequent usage, and reliance on L1-dominant communication. Using preliminary survey data and contemporary literature, the research explores cognitive and behavioral factors contributing to vocabulary erosion among digitally active adolescents. Findings indicate that excessive reliance on translation tools, frequent alternation between languages on social media, and passive engagement with visually dominated content such as video games and infographics significantly hinder vocabulary retention. Conversely, learners who actively produce English digital content, use spaced repetition techniques, and participate in structured peer interactions show substantially better long-term vocabulary retention. The study provides evidence-based recommendations for educators, including the integration of gamified learning technologies, promoting bilingual journaling, and fostering English-speaking environments both inside and outside the classroom. This research contributes to the existing literature on second language (L2) vocabulary attrition by highlighting practical strategies to sustain lexical competence among high school learners in bilingual, digitally enriched contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".