Beyond Orientation: Rethinking International Student Belonging in First-Year Experience Programs amid Globalizing Higher Education
Bibliographic record
Abstract
As higher education institutions increasingly globalize, international student inclusion in First-Year Experience (FYE) programs remains fragmented and marginal. While many universities offer separate orientations or supplemental support, these efforts often reflect a deficit-based model that treats internationalization as an add-on rather than a systemic imperative. This paper explores the intersection of FYE program design and international student belonging, drawing on institutional best practices, literature on student transition and adaptation, and the concept of “Internationalization at Home.” The paper outlines the historical evolution and core components of FYE in the U.S., then critiques its limitations when applied to international students. Seven strategic recommendations are proposed to reframe international FYE as an asset-based, culturally fluent, and institutionally embedded practice. These include proactive communication, integrated student affairs structures, inclusive curriculum and peer mentorship, culturally responsive development, and ongoing sustainable assessment. By moving beyond orientation and embracing intercultural belonging as a foundational design principle, institutions can better fulfill their internationalization missions and support the success of all students.
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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 | 0.039 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.002 | 0.006 |
| 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".