Attitudes of Engineering Students towards English Courses at Jadara University in Jordan
Bibliographic record
Abstract
English is a crucial language for students to communicate effectively in various aspects of their lives, including study, work, and social interactions. In developing countries, English serves as a second language to overcome language barriers and facilitate international communication. Engineering students need to develop verbal and written communication skills for their profession. English's global spread is influenced by historical, social, cultural, and economic factors. It is now the primary international language. English plays a significant role in the development of communication technological advances, and engineering students have increased exposure to technological English. However, current college methodologies do not adequately address students' communicative needs during their English degree in Jordan.Objective: This study explored the attitudes of engineering students at Jadara University in Jordan towards English courses.Methods: A survey was conducted to gather quantitative data, and statistical analysis, both descriptive and inferential, was employed to interpret the findings.Results: The results revealed a general positive attitude towards the importance of English proficiency, although various factors such as course content, teaching methods, and perceived relevance to the engineering field influenced student perceptions.Conclusions: Recommendations for curriculum improvement and teaching strategies are provided based on the study's findings.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".