Minding the gap: Lived experiences of university educators in the internationalized classroom
Bibliographic record
Abstract
The internationalization of Higher Education has been a key priority for universities across the Global West since the early 1990s as increasing cohorts of international students paying differential tuition fees have provided a buttress against simultaneously declining revenue streams. Although governments, institutions, faculties and departments disseminate strategies and policies, internationalization ultimately takes place in a classroom with students and educators sharing in the practices of teaching and learning. The aim of this research project was to explore how the internationalized Higher Education teaching and learning space is impacted and / or supported by the aspirations, rationales, assumptions and objectives of the many actors involved, focussing on the university educators who are internationally experienced and dedicated to the transformative opportunities of internationalization - and those who are more resistant. This thesis contextualizes relevant Higher Education internationalization policies in Canada and outlines research that aims to understand and problem-solve the experiences of educators in the internationalized classroom. Semi-structured interviews were conducted with university teaching staff in British Columbia, and the resulting data was analysed using thematic analysis. Findings indicate the imposition of structural and variable positions in governments, such as shifting immigration policy, and the institutional implementation of those positions has resulted in an unstable international student landscape. In addition to issues of language proficiency, teaching staff find that fluctuations in the proportion and makeup of the international student cohort substantially inhibit the attainment of acceptable learning outcomes. A divergence of experiences was found in systemic institutional support for both international students and teaching staff who often take it upon themselves to fill in the gap for their students. This study indicates that university educators bear an unequal portion of responsibility for international students and that well-intentioned recommendations for internationalized teaching and learning may be underestimating its layered complexity.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.036 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.010 |
| 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".