Bibliographic record
Abstract
Recently, the digitalization phenomenon has been trending upwards globally. This term has occupied all spheres of our lives, including education. Along with global tendencies and calls of the Industrial Revolution, 4.0 national projects outlined by the president in the particular project “Digital economy” have provided many impulses to the Digitalization of education.This research paper is mainly devoted to exploring digital education and digital learning in Russia's realities today. The author utilizes the current situation with lockdown and, therefore, distance education and learning to try to shed light on some aspects of educational Digitalization. The article provides a theoretical discussion of the irreversibility and necessity of Digitalization of education, its components, stages, structure, advantages, and disadvantages; of what has been done and what is to be done in this field. The author also provides empirical data of studying Kazan Federal University students in foreign language classes during distance education and learning period. Remarkably, the article offers some insight into students’ readiness for the digital era, evaluating their digital literacy and digital skills and competencies, their motivation to keep on studying while on distance, their abilities to take responsibility for their learning as well as some issues challenging students during distance learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".