Creating a Developmentally Appropriate Learning Environment in Preschool Education Institutions
Bibliographic record
Abstract
The article is devoted to the problem of modernization of preschool education in the conditions of modern educational reforms related to realization of humanistic paradigm.The authors describe the creating of developmental educational environment in establishments of preschool education.The article presents the theoretical substantiation and some technological statements of designing the educational environment as a factor in the development of the personality of a preschool child, in particular, theoretical and methodological principles of designing the educational environment of preschool education, namely: principles (pedagogical expediency, integrity, individualization, cognitive activity and independence), basic scientific approaches (systemic, synergetic, environmental, personality-oriented, activity-communicative), objective laws.The authors found that the theoretical interpretation of the concept the preschool child's personality development is based on the scientific position on its integrity and ability to make qualitative changes in the process of their own activities and communication with other people.It is determined that the development of the child's personality directly depends on the quality of content and procedural organization of the environment in which he is brought up. Key words: educational environment,
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".