Understanding How Program Factors Influence Intercultural Learning in Study Abroad: The Benefits of Mixed-Method Analysis
Bibliographic record
Abstract
With the proliferation of short term study abroad programs at institutions of higher education, there is a need for more rigorous assessment of how these pr ograms contribute to intercultural learning. This article presents a multi institutional comparative study of students’ intercultural learning in six short term study abroad programs in Canada and the U nited S tates , employing both quantitative and qualitat ive methods. The study combines pre and post IDI survey scores with a qualitative analysis of student writing to present evidence about the impact of specific program features on students’ intercultural learning, as well as an analysis of how the students themselves make sense of their experiences abroad. We argue that the extent of pre departure intercultural training has a positive relationship with intercultural learning outcomes. Additionally, we present evidence that service learning opportunities and intra group dynamics contribute to students’ intercultural competence. We conclude that mixed methods analysis provides the most effective way of identifying how different program factors contribute to intercultural growth, when that growth occurs in a pr ogram cycle, and how program leaders can provide effective intercultural interventions to best facilitate student learning abroad.
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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 | 0.079 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".