Focus on relationships and strengths: Engaging international learners online
Bibliographic record
Abstract
The shift to online courses during the global COVID-19 pandemic highlighted that teaching and learning online is an evolving practice for both students and educators. Notably, for international students, challenges with learning online can be more pronounced, as they are also adapting to cultures of their host country and expectations from their post-secondary institutions, while attempting to forge connections with their domestic peers. This paper describes several notable pedagogical interventions implemented by the author in her Canadian-based, online, asynchronous courses that have a high number of international students. These include repurposing office hours, reporting on student feedback, and incorporating Indigenous Ways of Knowing in the course content. Course evaluations and student testimonials are featured to demonstrate the effectiveness of these interventions in enhancing faculty-student relationships, student engagement, and individual and collective learning. Practical strategies that encourage international students to integrate life experiences into the curriculum are offered to help educators consider how they, in their own disciplines and teaching contexts, can stimulate curiosity and leverage students' prior knowledge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".