Preparing Australian nurse educators to teach international students
Bibliographic record
Abstract
Australia is the third largest provider of international education ,however, the preparedness of academics, teaching international students, may not have kept abreast with the growth in universities’ student enrolments in transnational teaching programs. The differences in Western and Asian cultures present challenges that can have a significant impact on the education of international students particularly in transnational teaching programs. As such, there is a need for educators to develop specific strategies to ensure positive student experience, learning and outcomes for international students. A design-based research methodology was adopted to construct, evaluate and endorse a professional development workshop that integrated reflective practice as the method of delivery. The professional development workshop sought primarily to assist nurse educators gain a deeper awareness of the cultural considerations associated with teaching international students and thereby develop strategies to effectively teach this cohort of students. Overall, the participants rated the workshop as highly effective, recognizing the relevance and appropriateness of the workshop content to their professional development needs and for clarifying appropriate and relevant strategies that could be employed when teaching international students. The professional development workshop participants affirmed that the workshop assisted them in developing understanding about cultural factors specific to a cohort of students they taught in Hong Kong. Further, these teachers believed that the integration of identified culturally sensitive teaching strategies in their practice was a direct result of their engagement in the workshops.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".