The Impact of Multilingualism on Productive Language Skills: Modelling Some Saudi Multilingual Learners
Bibliographic record
Abstract
The issue of whether a learner in multilingual education can achieve the same level of proficiency in two or more languages other than the native language is a problematic one. This study aims at investigating the impact of learning two languages other than the native language on the learners’ speaking and writing skills. To attain this aim, the researchers collected the scores of twenty-six multilingual Saudi learners on writing and speaking tests in the academic year 2020-2021. A qualitative and quantitative mixed research design is adopted to measure the performance of multilingual learners in the two languages: English and French. The results showed that there are statistically significant differences between the scores of the Writing Test (WT) in English and French in favor of the English Writing Test because the p-value (0.012) is less than (0.05). It is attributed to the fact that the learners’ mental faculty cannot be loaded with more than two writing systems. The results also showed that there are statistically significant differences between the scores of the Speaking Test (ST) in English and French because the level of significance (0.009) is less than (α = 0.05). It is attributed to the fact that the English sound system, because of rapid historical changes, becomes much easier for Saudi students than other phonological systems. The balanced students represent 26.9% of the total group of students. The study concludes that balancing between more than two languages is a little bit difficult as one language should dominate over the others.
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 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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".