The Impact of Native Language Use on Second Language Vocabulary Learning by Saudi EFL Students
Bibliographic record
Abstract
<p>This paper strives to explore the impact of Native Language use on Foreign Language vocabulary learning on the basis of empirical and available data. The study is carried out with special reference to the English Language Programme students in Buraydah Community College, Qassim University, Saudi Arabia. The Native Language of these students is Arabic and their Second Language is English. The participants in this research study are the post-secondary students of Buraydah Community College in Intensive Course Programme. The instrument used in this study was in the form of two tests. It is well known that in language assessment tests play a pivotal role in evaluating the EFL learners’ language proficiency. The use of native language as a semantic tool for assessing second language learners’ understanding shouldn’t be rejected altogether especially for the undergrad Saudi EFL (English as a Foreign Language) students. The outcomes of the study show that in learning the vocabulary of target language is significantly helped by the use of translation method of native language (Arabic) in understanding the meaning of novel words and expressions of foreign language (English). This method is widely welcomed by majority of the students of Buraydah Community College. It’s recommended to use this method in order to take the students directly to the core meaning of the word or expression. It also, sometimes, gives a sense of accuracy of the meaning of native language equivalents.</p>
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".