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Record W4289521112 · doi:10.5430/ijhe.v11n5p134

Internationalization of Curriculum in Omani Higher Education: Perceptions of Academic Staff in UTAS

2022· article· en· W4289521112 on OpenAlexvenueno aff
Ali Hubais, Muhammad Muftahu

Bibliographic record

VenueInternational Journal of Higher Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)CurriculumThematic analysisInternationalizationHigher educationMedical educationTypologyGlobeQualitative researchInstitutionQuality (philosophy)Political sciencePublic relationsPsychologyPedagogySociologyMedicineGeographySocial scienceBusiness

Abstract

fetched live from OpenAlex

The internationalization of the curriculum (IoC) has been a significant trend in higher education across the globe. However, there is a dearth of literature on this area of research in the Arab countries, including the Omani higher education context. As the key definition and conceptual frameworks of IoC have not been adopted in Omani higher education institutions (HEI), this qualitative study examined lecturers’ understanding of IoC in the Omani higher education context. This was carried out through the employment of the typology of IoC which was proposed by Edwards et al. (2003). To collect data, eight lecturers in an Omani university were interviewed and the thematic analysis of the data revealed that academic staff perceived IoC as important to the institution. Further, the study reported that there are fragmented IoC practices that are primarily based on the ad hoc practices of academic staff. Initiatives should be taken to develop a shared understanding of IoC at the institutional level and in all degree programs in the Omani higher education context. Some suggestions are brought forward for stakeholders to support IoC and help to ensure the quality of degree programs offered.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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.

Opus teacher head0.017
GPT teacher head0.322
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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