Translocal Community-Based Language Learning: A Digitally Mediated Online Travel Fair for Korean Language Learners
Bibliographic record
Abstract
ABSTRACT This pilot study examines a digitally mediated Community-Based Language Learning (CBLL) project—the Online Travel Fair—implemented across multiple North American universities to connect students from geographically distant institutions. Through synchronous virtual interactions simulating real-world travel-planning scenarios, participants designed multimodal brochures, delivered promotional pitches, and engaged in extended booth conversations, thereby using Korean purposefully across informational, persuasive, and interpersonal genres. Data from survey responses and qualitative feedback indicate that interacting with peers of comparable proficiency from other universities increased learners’ confidence in speaking Korean with unfamiliar interlocutors and fostered a sense of belonging to translocal communities of practice. Students emphasized not only the affordance of practicing Korean in meaningful, low-stakes contexts but also the value of forming interpersonal connections with peers beyond their home institutions, expressing a strong interest in continued cross-campus engagement. These findings suggest that digitally mediated CBLL can mitigate logistical and geographical constraints commonly associated with traditional community-engaged learning, while preserving its core pedagogical benefits of authenticity, reciprocity, and social participation.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".