Foreign Culture Awareness Needs of Saudi English Language Majors at Buraydah Community College
Bibliographic record
Abstract
Although many EFL learners have a command of internalized foreign language knowledge, they may have difficulty using this knowledge in different contexts. This is due to many interacting factors affecting their performance, mainly lack of target culture awareness. This study intended to identify the cultural aspects suitable to be integrated into the Saudi EFL classrooms, search for the sources upon which students get their knowledge of the target culture and examine their attitudes towards it. The study was administered to three main categories of subjects: students, teachers, and experts in the field of teaching EFL. The required data were collected using a Culture-awareness Diagnostic Test and a Culture-Awareness Needs Assessment Questionnaire. The results revealed the students’ need to learn about different aspects of foreign culture to develop their culture-awareness and that they depend mostly on audio-/visual media to learn about the target culture. The findings also showed that students possess positive attitudes toward the target culture and its people. Introducing authentic materials such as DVDs, video tapes, newspapers in the foreign language classroom was highly recommended for enhancing culture awareness and improving language proficiency. Therefore, the current research suggested modifying the entire EFL education programs to introduce culture in the foreign language classroom.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".