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Record W2165479527 · doi:10.5539/ass.v5n6p49

Curriculum as an International Text: Evaluation of Global Education from Junior High School Students' Knowledge and Attitude in Taiwan

2009· article· en· W2165479527 on OpenAlexvenueno aff
Su-ching Lin, Hsin-Yi Kung

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumScale (ratio)Multivariate analysis of varianceDescriptive statisticsMathematics educationPsychologyGlobal educationSocioeconomic statusSignificant differenceMedical educationPedagogySociologyGeographyMathematicsMedicineDemographyStatistics

Abstract

fetched live from OpenAlex

This study is interested in understanding curriculum as an international text and evaluating the connections between junior high school students’ global knowledge and attitudes and the required national curriculum in Taiwan. The study also examines whether the global knowledge and attitudes vary by demographic variables. By using the Global Knowledge Scale and Global Attitudes Scale, data were collected from 1,017 students in central Taiwan and analyzed by descriptive statistics, chi-square and one-way MANOVA. The results of this study revealed that, first, the global knowledge of the junior high school students was insufficient, but their global attitudes were positive. Second, there was no gender difference in global knowledge but there was a difference in global attitudes. Third, ninth graders held significantly the highest knowledge and attitudes than eighth graders and seventh graders. Fourth, there was a socioeconomic status difference in global knowledge and attitudes. Finally, students with overseas travel experience have better global knowledge and attitudes. This study suggests that practitioners and researchers need to find practical ways to improve global education including curriculum design and implementation, teacher preparation, school environment, and students’ assessment.

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)0.001
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 score0.677
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.019
GPT teacher head0.433
Teacher spread0.414 · 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

Citations1
Published2009
Admission routes1
Has abstractyes

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