Prospects for the Development of Design Thinking of Higher Education Applicants in the Culture and Art Industry in the Context of Digitalization
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".