L1-Induced Grammatical Errors Affecting Saudi Female EFL Students' Academic Writing: A Cross-Linguistic Study of Arabic Language Interference
Bibliographic record
Abstract
The influence of native language on second language learning has long been a hot topic in the fields of psychology and linguistics. Grammatical errors in EFL students' academic writing, specifically influenced by their mother tongue, are not a new phenomenon but rather an enduring one. This research examined the grammatical errors in Saudi EFL students' writing and evaluated whether these errors are daunting for learners due to L1 influence. Furthermore, it aimed to identify the underlying reasons for these errors and propose strategies for addressing this pinpointed issue. A mixed-methods approach was utilised in this study. An error analysis was conducted on thirty-two student essays, supplemented by a contrastive analysis to examine the distinctions between Arabic and English and to identify potential sources of interlanguage errors. A set of semi-structured interviews was conducted with six advanced female students to explore the extent to which L1 transfer influenced the errors predicted by the contrastive analysis. Results demonstrated that interlingual errors accounted for a higher percentage at 58.09% compared to intralingual errors at 41.91%, underscoring the significant impact of the mother tongue on L2 learning and writing. Interview analysis unveiled a noteworthy finding: the learners' mother tongue continues to have a pivotal impact on grammatical errors; L1-influenced grammatical errors could be attributed, at least in part, to a deficiency in Contrastive Linguistics (CL)-informed instruction.
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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 | 0.009 |
| 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 | 0.000 |
| 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".