DIGITAL LITERACY OF A TEACHER AS A FACTOR OF PEDAGOGICAL PERFORMANCE: FROM THEORY TO A DEVELOPMENT MODEL
Bibliographic record
Abstract
In the context of rapid digitalization of the educational environment, the concept of digital literacy of a teacher acquires not only methodological, but also strategic significance. A modern teacher is not just a transmitter of knowledge, but a curator of the digital experience of students, an intermediary between technologies and meanings formed in the educational process. However, in practice, high saturation with digital resources does not always lead to an increase in pedagogical effectiveness. A paradox arises: having access to digital tools does not guarantee their effective didactic use. The article is devoted to the analysis of digital literacy as a key factor in pedagogical effectiveness. The authors , relying on the experience of implementing digital solutions in schools and universities, propose a theoretically substantiated and practically tested model for developing digital literacy in teachers. The study reveals the components of digital competence, determines the mechanisms of their influence on academic success, engagement and development of meta-skills of students. The proposed model for the development of digital literacy is based on the principles of adaptive learning, personalization of professional growth and pedagogical reflection. Particular attention is paid to the role of digital mentoring, microlearning and self-diagnostic tools in the formation of sustainable digital practices. The model is integrated into existing processes of advanced training and can be scaled both at the level of an individual educational institution and within the framework of state policy in the field of digital transformation of education. Thus, digital literacy is viewed not as an isolated skill, but as a holistic pedagogical resource capable of radically transforming the quality of education and improving the effectiveness of teachers in the digital age.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".