Exploring Cultural Intelligence Skills among International Postgraduate Students at a Higher Education Institution
Bibliographic record
Abstract
Recognising the importance of Cultural Intelligence (CQ) is crucial to any Higher Education Institution (HEI) hosting international students. Thus, the investigation seeks to explore postgaduates experiences, perceptions, challenges and strategies in accommodating their friends’ from diverse cultural backgrounds in their daily academic and social lives. A qualitative research design was employed in that 15 international postgraduate students from 5 faculties were interviewed (using semi-structured interview protocol) in relation to their experience, understanding and behaviour towards cultural knowledge and skills. Rigourous thematic analysis following Braun & Clark (2006) was carried out. The main findings indicated that international students faced huge challenges during the acculturation and adaptation processes trying to be accepted or to blend in, unaware of Cultural Intelligence. Positive reactions from international students prevailed. External and internal factors posed as huge setback to their success in communication and studies. It can be concluded that with prior awareness and understanding of CQ and cultural diversity, international students could be more prepared in adjusting to academia, thus be more successful in their studies. The findings of this study are of paramount importance to HEIs, International and Student Admission offices around the globe.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".