Integrating Service-Learning in Korean Language Education: Three Case Studies
Bibliographic record
Abstract
ABSTRACT This study reports three pilot implementations of Community-Based Language Learning (CBLL) in advanced Korean language education at the University of Toronto between 2023 and 2025. The cases were designed to examine the feasibility and pedagogical value of integrating community-connected experiential learning into advanced-level curricula. The first case involved a student serving as a teaching assistant (TA) in a credit-bearing high school Korean language course. The second case examined three university students who worked as TAs in an intensive summer Korean language camp for secondary school learners, where they designed instructional materials and led tutorial sessions. The third case explored a remote, project-based collaboration with the Toronto office of the Korea Creative Content Agency (KOCCA), where a student produced a professional research report on the Canadian media industry entirely in Korean. Drawing on instructor observations and student reflection surveys, this study analyzes learning outcomes, affective factors, and implementation challenges across the three cases. Findings indicate that CBLL participation fostered pedagogical language use, intercultural awareness, and professional communication skills, as well as increased learner confidence, while also revealing challenges related to student anxiety, assessment design, and coordination of reciprocal community partnerships. The results demonstrate the feasibility of CBLL in advanced Korean programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".