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Educational Programs and Strategic Directions in the Context of Globalization

2024· article· en· W4405793424 on OpenAlexaff
Xi-Bao Huang, Xinya Zhang

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGlobalizationContext (archaeology)Political scienceBusinessGeography

Abstract

fetched live from OpenAlex

Globalization has become one of the key research topics in the field of global education. Educational curricula and strategies are gradually developing towards globalization and digitalization. This paper will study the current educational curriculum and the direction of future educational strategies under the background of globalization. Specifically, the core concepts, practical methods, and impacts of three distinctive educational models, namely, the Reggio education concept, the STEAM education system, and the IB international curriculum system, on contemporary education are studied. Through the study of these educational models, it is predicted that future educational strategies will develop in four aspects: emphasizing soft skills, increasing international education cooperation projects, promoting vocational skills training, and digital transformation. This paper adopts the literature research method, theoretical analysis method, case analysis method, and inductive summary method to conduct research. The research found that: the Reggio education concept, STEAM education system, and IB curriculum system promote the all-round development of children's independent learning and innovation ability, critical thinking, and global vision; future educational strategies will also tend to break the boundaries of disciplines and cultivate talents who dare to innovate and master multiple skills. This paper expands the conjecture about future educational strategies and is committed to helping international students improve their global adaptability.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.375
Teacher spread0.345 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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