Intercultural Competence of Chinese College English Teachers: A Comprehensive Study
Bibliographic record
Abstract
This study investigates Chinese college English teachers’ intercultural competence (IC) through mixed-methods research involving 69 survey respondents and four interviews. Findings reveal systemic challenges in integrating cultural instruction, including teachers’ self-perceived knowledge gaps (39%), pedagogical skill deficiencies (36%), and curricular misalignment with policy mandates. While national reforms emphasize IC development, classroom practices remain constrained by exam-oriented priorities, limited training (only 26% received in-service IC training), and textbook biases favoring Anglo-American cultures. Quantitative analysis shows teachers’ confidence negatively correlates with teaching experience, but positively with education level, suggesting advanced academic training enhances IC integration capabilities. Qualitative data highlight tensions between ideological-political curriculum requirements and authentic intercultural pedagogy, with teachers creatively adapting content to balance cultural perspectives. The research identifies two critical gaps: (1) policy-implementation disconnect in cultural education, and (2) insufficient professional development despite training demands. Recommendations emphasize holistic reforms: expanding workshop-based teacher training, redesigning textbooks with balanced cultural representations, and aligning assessment systems with IC objectives. This study underscores the necessity of addressing both institutional barriers (time constraints, testing systems) and individual competencies to bridge China’s intercultural education aspirations and classroom realities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".