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Record W2943615554 · doi:10.5539/ach.v11n2p26

Re-conceptualizing the Jordanian Art Education Curricula: Suggested Entries for Teaching Discipline-Based Art Education Theory

2019· article· en· W2943615554 on OpenAlexvenueno aff
Bassam N. Al-Radaideh, Raed Al-Share, Asem Obidat

Bibliographic record

VenueAsian Culture and History · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsVisual arts educationCurriculumCreativityArt criticismDisciplineMathematics educationArt methodologyTeaching methodCriticismCivilizationPedagogyPsychologySociologyContemporary artArtVisual artsSocial sciencePolitical scienceThe artsLiteratureArt history

Abstract

fetched live from OpenAlex

The curricula of art education in the elementary and secondary schools of Jordan is limited to teaching technical skills for making art, and students did not receive tangibleeducation about history of art, aesthetic, and critical aspects of art. This study identified the theory of Discipline-Based Art Education (DBAE) and its significance in teaching art, and it provided suggestions for teaching history of art, criticism, aesthetic and artistic production. Furthermore, the study justified the possibility of implementing the DBAE approach in Jordan art education curricula. The research revealed that DBAE theory improved and elevated art education to a new level because the four disciplinary content area played a significant role in the development of essential knowledge and skills in the art such as developing the creativity, appreciation, understanding and learning about the role and function of art in human civilization. The study recommends to include the components of DBAE to art education instruction in Jordanian curricula.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0050.011
Scholarly communication0.0100.008
Open science0.0020.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.246
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations2
Published2019
Admission routes1
Has abstractyes

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