Negotiating differences in academic preparedness among transnational students in higher education
Bibliographic record
Abstract
“Students these days don’t know anything,” commented a faculty member in a focus group. But is it that students don’t know anything, or that faculty members are unfamiliar with what students do know? This is no small issue, as the acceleration of internationalization in higher education and the broader processes of globalization have led to increased numbers of students with general education profiles that differ from those of the instructors and their domestic students. These transnational students—not only international students (paying international tuition), but also permanent residents and citizens —received their primary and secondary (and, possibly, undergraduate) educations outside of Canada, and instructors often lack an awareness of their educational backgrounds and experiences. Emerging from a study of the everyday instructional needs of full-time college and university faculty, this paper presents the results of a systematic review of the literature on the general education of students from four regions that are sources of international students and immigrants, and, therefore, prepared students on campus: China, India, the Middle East, and Latin America. The research not only identifies the curriculum that these students followed, but also the impact of educational reforms on the teaching styles and learning skills emphasized in these systems, and describes the implications for Canadian instructors.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".