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Record W4392775255 · doi:10.5539/hes.v14n2p1

Fostering Global Competence in Teacher Education: Curriculum Integration and Professional Development

2024· article· en· W4392775255 on OpenAlexvenueno aff
Dech-siri Nopas, Chakree Kerdsomboon

Bibliographic record

VenueHigher Education Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)Professional developmentCurriculumFaculty developmentCurriculum developmentPedagogyPsychologyMathematics educationHigher educationPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.076
GPT teacher head0.450
Teacher spread0.374 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2024
Admission routes1
Has abstractyes

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