Internationalization of Higher Education: International Students’ Perspectives
Bibliographic record
Abstract
Abstract The enrollment of international students in higher education has grown significantly worldwide, bringing both opportunities and challenges. These students often face obstacles such as language barriers, academic difficulties, cultural adjustment, and limited engagement. As a result, there is an increasing need to internationalize teaching and curriculum to foster a greater sense of belonging for international students while also equipping all students—domestic and international—with the intercultural competencies necessary to thrive in a global society. Internationalizing the curriculum enhances inclusivity and prepares students to become global citizens by developing their cultural awareness, communication skills, and global perspectives. This approach not only supports international students’ academic and social success but also enriches the learning experience for all students. This chapter explores effective strategies for internationalizing teaching and curriculum, drawing from both faculty perspectives and student experiences. It examines how inclusive pedagogical practices and culturally responsive content can create more engaging, equitable, and globally informed classrooms. By incorporating diverse voices and worldviews into course design and delivery, educators can better address the needs of international students and promote intercultural learning across the academic community.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".