International Education Online? A Report on Six Canadian Case Studies
Bibliographic record
Abstract
While the benefits of international education are beyond question, established international education (IE) activities remain beyond the reach of most Canadian students. Can information and communication technologies (ICTs) expand access to international education in a meaningful way? This report describes highlights of case studies of six diverse and innovative Canadian adventures with online IE: At the University of British Columbia, the online course ‘Working in International Health’ contributes to internationalization of the curriculum and prepares students for work in the developing world. Mount Royal College in Calgary leads an international ‘Consortium on Design Education’ online design challenge to introduce students to international and intercultural elements of design. At Ryerson University, integration of a “Virtual Law Firms” experiential online activity gives students first-hand experience of the world of international business law. The new ‘University of the Arctic’ makes use of ICTs to connect students from over 40 institutions in eight Arctic states. ‘Introduction to Ethnomusicology’ at the Université de Montréal demonstrates Québec’s leadership of international ICT initiatives in the Francophone world, and challenges Canadian and African students to rethink their cultural perspectives on music. And the ‘e-Learning for Business Innovation and Growth’ project in Newfoundland and Labrador extends international learning to lifelong learners.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".