Features of the Application of Smart Technologies for the Development of Various Directions of Design Education
Bibliographic record
Abstract
The current increase in requirements for designers in various professional fields is due to the challenges posed by globalization and the informatization of modern society as the main trends in its development. The integration of modern technologies into the structure of designers' professional activities requires a revision of approaches to the professional training of both future design professionals and artists in general. The article aims to study the theoretical foundations and certain practical aspects of the application of SMART technologies in the design education of HEIs of technical and humanitarian orientation. Methodology. The study applied analytical and bibliographic, systemic and structural, comparative, logical, and linguistic methods, analysis, synthesis, induction, and deduction in the processing of scientific information on the use of SMART technologies in design education. Moreover, the methods of abstraction and idealization served to study and process statistical and analytical data. Analysis, synthesis, induction, and deduction helped to study the scientific literature and summarize the results of the survey. Results. The study examined the theoretical foundations and results of a survey on the concept and main trends of design education, as well as the prerequisites, patterns, and directions of development of SMART technologies in terms of assessing the experience of their use in the educational process in teaching design specialties.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".