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Record W4414331893 · doi:10.1080/2331186x.2025.2560056

Dietary education in Japanese and Korean middle schools: contents and pedagogical styles from a constructivist perspective

2025· article· en· W4414331893 on OpenAlexaff
H. A. Jung, Young-Eun Lee, Soo-Hee Lee

Bibliographic record

VenueCogent Education · 2025
Typearticle
Languageen
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsConstructivist teaching methodsSocial constructivismCurriculumPerspective (graphical)Context (archaeology)Constructivism (international relations)AccreditationTeaching method

Abstract

fetched live from OpenAlex

The purpose of this study was to compare home economics textbooks used in Japanese and Korean middle schools from the constructivist perspective to examine whether home economics of both countries can promote constructivist learning. For this, the educational contents of textbooks were analyzed from the constructivist perspective. Pedagogical styles of the texts and exercises were classified into four types: Neutral informative, injunctive, persuasive, and participative. The study data consisted commonly used Japanese textbooks (n = 3) and Korean textbooks (n = 6). Compared to Japanese textbooks, we found that Korean textbooks need contents such as context reflecting historical, cultural, and social situations, which are learning elements from the constructivist perspective. Korean textbooks contained limited dietary problems, and these only related to individuals and families. In both countries, the pedagogical styles were usually participative style in exercises (Japan: 61.5%, Korea: 60.4%), but in texts, the proportion of participative style, reflecting the social constructivist perspective, was low (Japan: 5.7%, Korea: 0.7%). It is recommended that systems be established to support the successful implementation of constructivist learning by providing clearer textbook-writing guidelines tailored to constructivist learning, textbook accreditation standards, and curriculum content that reflects a constructivist perspective in teacher education programs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.768

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 head0.080
GPT teacher head0.389
Teacher spread0.309 · 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
Published2025
Admission routes1
Has abstractyes

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