Exploring the Impact of Experience, Department, and Course on the Perception of the Internationalization of Curriculum (IoC) Among Staff at UTAS-Salalah
Bibliographic record
Abstract
The internationalization of Curriculum (IoC) is a significant aspect of higher education to prepare graduates for a globalized world. This study explores academic staff perceptions of IoC at the University of Technology and Applied Sciences-Salalah (UTAS) in Sultanate of Oman, addressing a gap in the literature on IoC in non-Western contexts. Furthermore, the study explores the perspectives of academic staff regarding the implementation of IoC at different levels within the institution, department, and courses. Not only this, it also explores how these perspectives may vary based on the number of years of experience, as well as the differences in perceptions among different departments. Additionally, the study explores the perceptions of academic staff regarding the three fundamental levels of IoC, namely awareness, competence, and expertise. A quantitative cross-sectional survey was conducted among all academic staff at UTAS. Data were analyzed using descriptive statistics, ANOVAs, and paired t-tests. Results revealed that perceptions of IoC did not vary significantly based on years of experience or across departments. However, perceptions varied significantly across the three core levels, with expertise rated highest and awareness lowest. Strengths and weaknesses were identified within each level. This study contributes to the understanding of IoC in non-Western settings and informs institutional policies and practices. A holistic and contextually sensitive approach is needed, involving staff development, institutional support, and cross-disciplinary collaboration. Finally, the findings represent a significant step towards a more inclusive understanding of IoC in the Omani context and beyond.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".