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Record W4283718512 · doi:10.5430/jct.v11n5p28

Significance of Argumentation Tasks in Vietnamese Geography Textbooks Following the Competency-based Curriculum Reform

2022· article· en· W4283718512 on OpenAlexvenueno aff
Thành Tâm Nguyên, Alexandra Budke

Bibliographic record

VenueJournal of Curriculum and Teaching · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeography Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsArgumentation theoryVietnameseCurriculumCompetence (human resources)PedagogyMathematics educationIntercultural competencePolitical scienceSociologyPsychologyEpistemology

Abstract

fetched live from OpenAlex

This article focuses on the promotion of argumentation skills in Vietnamese geography teaching, which are not only relevant for understanding subject contexts but also for evaluation and critical reflection processes. An earlier study in this context showed that limited argumentation tasks are incorporated into the central medium of instruction, the textbook, and that this competence is rarely promoted in the classroom (Nguyen, 2018). However, a curriculum reform and revision of textbooks is currently taking place in Vietnam, alongside a liberalisation of the textbook market. As the main goal of the reform is competence orientation, this article examines the extent to which the importance of promoting argumentation competences through specifications in the new curriculum and tasks in new textbooks have increased in comparison to the previous study. The results suggest that there are few developments in this area, which are further discussed in the conclusion, in the context of the global challenges for implementing competence orientation through curricular.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.092
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.314
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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