Developing intercultural competence through a linked course model curriculum: Mainstream and L2‐specific first‐year writing
Bibliographic record
Abstract
Institutions of higher education in the United States continue to witness a dramatic shift in the spectrum of diversity in their student populations. Multiple variables of difference that mixed student demographics bring to university campuses make internationalization work necessary both inside and outside the classroom. Internationalization of higher education is a collaborative responsibility academic and nonacademic programs should share to facilitate the integration of various student populations within the broader culture of the university. However, there are few, if any, models for internationalizing introductory courses required of a large percentage of the student body, such as first‐year writing (FYW). In this article, the authors propose and argue for an intercultural competence–oriented approach to internationalizing writing programs through a linked course model curriculum that pairs international and domestic students in separate second language–specific and mainstream FYW classes. The linked course model curriculum develops and assesses students’ intercultural learning and writing skills as core learning outcomes. This article presents the curricular design and interventions, the research design of the study conducted across three semesters of curriculum implementation, and the reflective writing results from the pilot semester to communicate the preliminary effectiveness of this curricular model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".