Importance and Methods of Cultivating Cross-cultural Communication Skills in Korean Language Teaching
Bibliographic record
Abstract
With the continuous deepening of globalization and the strengthening of communication and cooperation among countries, the cultivation of cross-cultural communication (CCC) skills has become increasingly crucial in vocational Korean language teaching. As an important component of cultivating future vocational talents, Korean language teaching in vocational colleges places higher demands on students' CCC abilities. Korean communication is no longer limited to language skills, but requires students to have the ability to understand, respect, and effectively communicate in a cross-cultural environment. Therefore, vocational Korean language teaching should be committed to providing students with more comprehensive and profound cultural literacy, so that they can be competent for work and cooperation in different cultural backgrounds on the international stage. This article explores the connotation of CCC ability and its application in Korean language teaching in vocational colleges through literature review, questionnaire survey, and data analysis. The accuracy rates of nonverbal communication behaviors and conversation principles are relatively low, at 24.15% and 10.13%, respectively. The accuracy rate of English culture-laden words in English is the lowest, only 5.20%. Under the traditional teaching model, students lack CCC skills, and there is an urgent need to improve teaching concepts and models, increase the coverage of cross-cultural content, and enhance teachers' cross-cultural educational abilities. This article proposes specific strategies for cultivating students' CCC skills in vocational Korean language teaching, in order to provide reference and inspiration for relevant teaching practices.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".