The Use of Arabic in Teaching and Learning Foreign Languages for Saudi Language Learners: A Case Study
Bibliographic record
Abstract
This paper explores the effectiveness of first-language (L1) use in teaching and learning Chinese as a foreign language (CFL) from the perceptions of learners and instructors in Saudi Arabia. Although this issue has been studied in the context of English as a foreign language (EFL), less commonly spoken languages in Saudi Arabia have not received as much attention from scholars. 60 undergraduate students who have passed beginner levels in the Department of Chinese at the University of Jeddah were given a questionnaire. Five focus groups were organized, each consisting of five students, and one that included the only two instructors in the department, to gather data about participants' attitudes, perceptions, and experiences related to the topic. The findings suggest that using L1 for instruction and communication in CFL classrooms is essential in the first stages of learning the language, and that the learning process can take two years, due to the uniqueness of the language. Although the systematic and purposeful use of L1 has already been encouraged in the context of learning EFL, this approach could potentially be used in CFL classrooms after the students have successfully acquired basic language skills.
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 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 | 0.002 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".