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Record W4205701757 · doi:10.17762/de.vol2022iss1.8735

Modern Approaches to the Teaching of Fine Arts

2022· article· en· W4205701757 on OpenAlexvenueno aff
Ahmedbekova Maxpuza Kakharovna Et al.

Bibliographic record

VenueDesign Engineering · 2022
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Visual Art
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsCreativityMathematics educationVisual arts educationPaintingArts in educationFine artPsychologyTeaching methodVisual artsComputer sciencePedagogyArt

Abstract

fetched live from OpenAlex

The present research focuses on modernizing the approach to learning and teaching the visual arts in teaching practice, as well as examining the performance of an interactive approach to learning and teaching in visual arts classes with the use of a combination of general and specific (visual arts) teaching methods. The study uses quantitative analysis of data on the basis of results obtained from a pedagogical experiment. The subjects of the research were 285 second- and fourth-grade students from four primary schools in the city of Rijeka, Croatia. Paintings made by the students in the initial and final stage of the pedagogical experiment were evaluated. The research results confirmed the hypotheses about the positive effect of interactive approaches to learning and teaching on the following variables: (1) knowledge and understanding of visual arts terms, (2) abilities and skills in the use of art materials and techniques within the framework of planned painting tasks, and (3) creativity in solving visual arts problems. The research results can help shape an optimised model for the planning and performance of visual arts education, and provide guidelines for planning professional development and the further professional education of teachers, with the aim of establishing more efficient learning and teaching of the visual arts in primary school.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0020.007
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.137
GPT teacher head0.228
Teacher spread0.091 · 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 designNot applicable
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

Citations4
Published2022
Admission routes1
Has abstractyes

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