Fostering Global Competence in Teacher Education: Curriculum Integration and Professional Development
Bibliographic record
Abstract
In an increasingly interconnected world, global competence in teacher education is paramount. This qualitative study delved into the perspectives of teacher education students, exploring their insights and experiences regarding integrating global competence into educational practice. Through in-depth interviews and thematic analysis, several key themes emerged, shedding light on the multifaceted nature of global competence. Participants emphasized the necessity of understanding diverse languages, cultures, histories, and geographical landscapes, highlighting their role in fostering intercultural empathy and appreciation. Moreover, the study elucidated the challenges faced in promoting global competence within the educational system, including curriculum constraints and limited opportunities for experiential learning. However, amidst these challenges, participants identified various strategies and recommendations for enhancing global competence in teacher education. These recommendations included integrating cross-cultural content into the curriculum, providing experiential learning opportunities, investing in professional development for educators, and fostering partnerships with international educational institutions. By addressing these recommendations, educators and policymakers could pave the way for a more inclusive and globally-minded educational system, equipping students with the skills and knowledge necessary to thrive in an interconnected world.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".