FUTURE TEACHER TRAINING FOR THE USE OF AUGMENTED REALITY IN THE EDUCATIONAL PROCESS OF A PRESCHOOL EDUCATION INSTITUTION
Bibliographic record
Abstract
The article substantiates the importance of future teacher training for the use of augmented reality (AR) in the educational process of a preschool education institution (PEI). Scientific sources on the problem of AR application in the field of education are analysed. The aspects of the research of the problem of AR application are defined in the field of education done by modern foreign and national scientists, in particular, the use of AR applications in education; introduction of 3D technologies, virtual reality (VR) and augmented reality in the educational process of preschool and primary school; application of 3d technology, virtual and augmented reality in a higher education institution; increasing the efficiency of training and motivation of students on the basis of using AR applications in smartphones; the formation of reading culture by means of augmented reality technology; prospects for the use of augmented reality within the linguistic and literary field of preschool and primary education. The specifics of publications of fiction works with AR applications are analysed that are appropriate to use in work with preschool children; the possibilities of books for preschool children created with the help of augmented reality technology are demonstrated. The possibilities of using AR in work with preschoolers are considered. The urgency of the use of AR for the effective education and development of preschoolers is determined. The problems of application of AR in the educational process of modern national PEI are outlined. A method of diagnostic research of the level and features of readiness of future teachers to use AR in the educational process of PEI has been developed. Criteria and indicators are defined, the levels of development of the main components of the studied readiness (cognitive and active) and the indicated readiness as a whole are characterized. The following points are proved: insufficiency and deficit of its formation of future teachers of the field of preschool education; inconsistency between the peculiarities of future teacher training to use AR in professional activities and modern requirements for the quality of educational process in PEI; the need to develop and implement a model for the formation of the studied readiness of future teachers of the institution of higher pedagogical education. A step-by-step model of formation of readiness of future teachers to use AR in the educational process of PEI has been developed.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".