Enhancing Technical English Proficiency: A Video-Based Approach for Female Students at Industrial Colleges in Saudi Arabia
Bibliographic record
Abstract
This research used constructivist theory and socio-cultural perspectives to investigate how video-based learning affected the technical English ability of female students at Yanbu Industrial College. The study involved 60 Saudi female ESL students at the intermediate English level, who were chosen through convenience sampling to participate. The study promoted tailored strategies to enhance language acquisition by highlighting the need for focused interventions to close the gap between acknowledged advantages and the real use of video resources. Utilizing a mixed-method approach, the study assessed students' perceptions of and experiences with video materials. Only 15% of respondents actually used instructional videos, despite the fact that a considerable 92% of respondents acknowledged the need for help in Technical English. This indicates a major disconnect between perceived requirements and actual use. During the intervention, observations revealed a changed classroom setting that featured dynamic, multimedia-driven learning in place of conventional teaching techniques. In order to improve language competency, this research highlighted the need to align identified demands with useful resource utilization. It also offers critical insights into effective instructional methodologies.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".