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Record W2113621573 · doi:10.5206/eei.v22i2.7697

Linking the Nature of Secondary School Students who are Highly Artistic with Curriculum Needs and Instructional Practice

2012· article· en· W2113621573 on OpenAlexvenueno aff
Victoria Marie. Visconti

Bibliographic record

VenueExceptionality Education International · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityCurriculumPsychologyVisual arts educationThe artsMathematics educationPedagogyQualitative researchMetacognitionCurriculum developmentCritical thinkingExploratory researchCognitionSociologyVisual artsSocial psychologySocial science

Abstract

fetched live from OpenAlex

This exploratory qualitative investigation examined the nature of 7 secondary school students who are highly artistic to link visual arts programs that best ac-commodate their learning needs. Instrumentation for data collection included 1 questionnaire, 3 in-depth semistructured one-on-one interviews, artwork docu-ments, observations, and field notes. Findings related to creativity, motivation, social and emotional perspectives, and cognitive processes supported the signifi-cance and benefits of visual arts in student growth. Results identified the development of critical thinking, problem-solving skills, risk-taking, meeting chal-lenges, transferability of skills, extending local and world connections, and environmental and societal concerns. Through artwork production and insight into their needs, students conveyed valuable suggestions for programming en-hancements and visual arts classroom settings. These findings are meaningful for educators and curriculum developers as they distinguish the importance of diver-sified and differentiated learning opportunities in engaging students who are highly artistic to meet their optimal potential, and suggest implications for educa-tional practice.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.997

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.295
Teacher spread0.284 · 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.

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
Published2012
Admission routes1
Has abstractyes

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