The Development of Innovative Media Education Styles in the Era of Information and Communication Technologies
Bibliographic record
Abstract
The new era of the 21st century is characterized by the rapid pace of digitalization of society and the development of information and communication technologies (ICTs). ICTs are transforming the basics of educational activities from the physical environment to the virtual one. There is a similarity between technology and media in content and strategic context. The media actively influence the public opinion, and information and communication technologies are used to increase the impact on academic performance. Therefore, there is a need for a critical analysis of information reality in order to develop the competence in future generation. The article provides the study of the process of development of innovative media education styles, which are effective in educational activities for the formation of a competent future generation capable of critical analysis of the information. The study of the formation of innovative media education styles was based on the Synyavsky’s communicative and organizational skills measurement methods in order to diagnose the main aspects of educational activities in the innovative context, Milman’s personal motivation technique, survey to determine the competency criterion of media education. A pedagogical experiment was conducted as part of the study. The results of the study became the ground for determining the content of innovative media education styles as an alternative to modern forms of education. Innovative media education styles are formed due to the influence of ICTs on educational activities. The obtained data were processed in SPSS 18.0.1.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".