Bridging the Language Gap: Supporting Low-Level English Diploma Students at KFU
Bibliographic record
Abstract
English proficiency is important for academic and professional success, particularly in disciplines such as accounting, finance, and computer studies, where technical language and global communication are critical. However, many diploma students enter higher education with low English proficiency, creating challenges in understanding course content, participating in discussions, and completing assignments effectively. This study explores a structured, multi-faceted instructional approach aimed at improving English skills among first-year diploma students in Saudi Arabia. A mixed-methods research approach will be employed, involving surveys and interviews with students and instructors from diploma programs in Saudi Arabia. The study employs a quasi-experimental design, comparing an experimental group receiving targeted interventions—including contextualized learning, scaffolded instruction, task-based activities, technology-enhanced learning, and peer collaboration—with a control group following the standard curriculum. Quantitative data are collected through pre- and post-tests, while qualitative insights are gathered via student interviews and classroom observations. The results indicate that students in the experimental group demonstrated significant improvements in reading comprehension, writing accuracy, speaking confidence, and listening skills, compared to the control group. The findings have important implications for curriculum design, suggesting that integrating career-oriented language learning with scaffolded support and digital tools enhances both language proficiency and professional readiness.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".