Empowering Faculty to Support International Students to Overcome the Academic Challenges Faced in Higher Education
Bibliographic record
Abstract
The landscape of higher education is increasingly globalized with greater mobilization of international students (IS), which in turn requires the most effective teaching practices. Canada is a country with the most rapidly growing IS population that face multiple challenges. The realities faced by IS are different than those of domestic students. These facts compel the faculty to enhance their teaching practices and leadership role as pivotal components in addressing the learning needs of IS, while supporting them to overcome academic challenges. In this Dissertation-in-Practice (DiP), I investigate the lack of means of faculty to address the academic challenges faced by IS and explore the most appropriate solution to empower faculty at a medium-sized organization (OESBC; a pseudonym) that is a provider of academic support for IS in degree programs in a large western city in Canada. Empirical evidence suggests that the most suitable solution to address a first-order incremental change at this type of organization to empower faculty is a Community of Practice as a pilot project. Leading this change using an integrative approach of interpretivism, collaborative and situational leadership, Kotter’s model, targeted communication, knowledge mobilization plan, and the PDCA cycle will support the change team to attain the goals for faculty and indirectly impact the IS’ performance and social justice issues. With the support of the branch manager and future participants, I will lead the complexity of the change implementation plan, and strengthen the faculty leadership around innovative teaching practices to achieve the envisioned future for OESBC.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".