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Record W4290466006 · doi:10.5430/jct.v11n5p196

Prospects for the Development of Design Thinking of Higher Education Applicants in the Culture and Art Industry in the Context of Digitalization

2022· article· en· W4290466006 on OpenAlexvenueno aff
Liudmyla Dykhnych, Олена Каракоз, Levchuk Yana, Svitlana Namestiuk, Yasynska Olena

Bibliographic record

VenueJournal of Curriculum and Teaching · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicDiverse Scientific Research in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityBachelorContext (archaeology)Design thinkingDesign educationNoveltyEngineering ethicsEngineering managementEngineeringSociologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

The rapid development of science, technology, and digital multimedia has made adjustments in all areas of design. Its ramifications and forms are deeply influenced by digital media, making new demands on teaching. A renewed society regulates new modes of teaching to produce applicants capable of adapting to the development of modern society and responding to the needs of the market. The article aims to present the impact of the development of modern digital media technologies on the teaching of modern fashion design at Kyiv National University of Culture and Art and to analyze the methodological problems of design thinking. The novelty of the work is the consideration of a new mode of teaching that is suitable for digital media art design from the perspective of design thinking methodology. The result of the work is the connection between the modes of design and the development of creative projects in the perspective of the model of homogeneous synthesis of the use of design thinking methodology and digitalization. The feasibility of the methodology synthesis is revealed in the results of the experiment, which involved 30 students from three faculties of Kyiv National University of Culture and Art. The groups took a combined bachelor's and master's degree course in design engineering, aimed at developing the best final project. Students combined this course with incorporating a design thinking approach. The best project was demonstrated by the group that focused the most on the use of design thinking and creativity and was guided in the design development by one of the basic components of design thinking, empathy. Taking the perspective of incorporating design thinking into the learning process can identify the next steps that can assess a future professional's life trajectory as well as address topics such as recognition, memory, and future development perspectives.

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)0.028
metaresearch head score (Gemma)0.019
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: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0050.012
Scholarly communication0.0110.007
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.026
GPT teacher head0.296
Teacher spread0.271 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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