Beyond Language: Exploring the Hidden Curriculum's Contribution to the Language Development of English Language Learners
Bibliographic record
Abstract
The hidden curriculum involves the implicit values, behaviors, a collection of concepts, and mental processes that are conveyed through the educational process, often outside the formal curriculum. The current study explored the influence of the hidden curriculum on the development of English language learners in Saudi Arabia. A questionnaire was designed to gather school principals' opinions on English teachers' influence on learner development across six domains: National Identity and Loyalty; Professional Commitment and Discipline; Ethical and Professional Behavior; Digital Awareness and Cyber Safety; Learning and Self-Education; and Intellectual Awareness. The research involved a sample of 168 school principals. The findings indicated that the overall mean score across the six domains is notably high. Additionally, the findings showed no statistically significant differences in the perceptions of school principals concerning the role of English language teachers in the hidden curriculum, regardless of teacher-related factors such as academic qualifications, years of experience, or gender. The study highlighted the crucial role of English language teachers in transmitting values, attitudes, and behaviors through the hidden curriculum, extending their contributions beyond linguistic competence to encompass national allegiance, ethical conduct, digital citizenship, self-education, and intellectual discernment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".