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Record W2061906818 · doi:10.1386/eta.5.2and3.241/1

Using an interactive art education application to promote cultural awareness: a case study from Turkey

2009· article· en· W2061906818 on OpenAlexaboutno aff
Suzan Duygu Bedir Erişti

Bibliographic record

VenueInternational Journal of Education through Art · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsViewpointsAsynchronous communicationInteractive LearningVideoconferencingThe InternetMultimediaCultural learningPsychologyInteractive mediaAsynchronous learningPedagogyMathematics educationVisual artsComputer scienceTeaching methodArtCooperative learningSynchronous learningWorld Wide Web

Abstract

fetched live from OpenAlex

In this study interactive technologies were used to promote cultural awareness. A series of five interactive art lessons was developed and carried out with 47 primary students at a private school in Turkey. The lessons included use of the Internet, asynchronous video conferencing, e-mail chatting. The students participated in an interactive learning experience with peers in Canada over a period of three weeks in which they exchanged cultural images and an instructional CD. They were interviewed later to examine their impressions. Most students stated that the interactive art lessons involving audio-visual technologies had encouraged learning and promoted higher levels of understanding. A considerable number had changed their views about culture. They liked learning about student viewpoints from other countries and mentioned that combining traditional and new technologies this way increases cross-cultural interaction.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.073
GPT teacher head0.407
Teacher spread0.333 · 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 designQualitative
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

Citations7
Published2009
Admission routes1
Has abstractyes

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