Personalization of Art Students' Training in the Context of the Transition to the Digital Economy
Bibliographic record
Abstract
Today, one of the main resources for the effective functioning of many political, sociocultural, and communication processes is transition to the digital economy and digital reality in general. Informatization, computerization, automation naturally integrate into the artistic culture and transform it into a digital one. As a result, changes in the professional field of art definitely require changes in both the content, methodology and technological base of art education in the context of transition to digital economy. At the same time, digital technologies are a factor in the modernization of the higher education system, and its tool. There is an objective need to individualize and personalize educational technologies. In turn, digitalization creates the foundations by which these processes can be implemented. The article specifies pedagogical conditions for personalization of art students' training: activating self-education and self-development mechanisms through the creation of individual educational routes; enriching the informational educational and methodological base to maintain an individual format for studying the content of artistic culture; adopting a personal position of an adviser and a facilitator by the university teacher, which contributes to the design, stimulation and reflection of the personal and competent development of art students. The reliability of conclusions made within this theoretical study is confirmed by the positive results of experimental work.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".