Modernization of Education Programs and Formation of Digital Competences of Future Primary School Teachers
Bibliographic record
Abstract
Modernization of education programs is carried out through the digital technologies integration into the education system, in particular, into the primary education system. This leads to the integration of courses and disciplines into curricula for the development of digital competences of future primary school teachers. The purpose of the investigation was to study the features of modernization of education programs in order to form digital competences of future primary school teachers. 48 scientific publications have been examined, studied and systematized, where the integration of disciplines for the study of digital competences into the educational process of teaching future teachers is considered; consequently, three dimensions of modernization of curricula have been investigated. The first one concerns the development of general digital competences, the second one - the competence to integrate technology into educational practice, the third one - professional digital competence. It has been revealed that general digital competences are consistent with the context of the education system. Competence to integrate technologies into teaching practice focuses on the context of integration, preparing future teachers for the opportunity to use technology in future teaching of pupils, critically assess the use and teach children, using digital devices in education process. The academic paper proposes the concept of professional digital competence as the ability of a teacher to work in the context of a school education system with a digital form of education, including the process of teaching, managing digital learning environments and professional activities of a teacher. Further research should additionally explore professional digital competence in the framework of primary teacher education.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".