THE FEATURES OF TEACHER TRAINING FOR MEDIA EDUCATION OF STUDENTS IN THE DEVELOPED ENGLISH-SPEAKING COUNTRIES
Bibliographic record
Abstract
Media literacy deserves a special place in teacher education, as it stimulates critical thinking, including a variety of reading, writing and speaking skills, the use of computer technology and the decoding of various types of information. In the article, the author analyzes the features of teacher training for media education of students in developed English-speaking countries. Media literacy, introduced into training programs, can be very useful and effective. Preparation for the development of media literacy of teachers can be carried out in the process of advanced training, educational psychology courses, basic training courses and teaching practice. Media literacy contributes to critical thinking, focusing on social issues, understanding the branches of knowledge and children, and shaping teacher professionalism. Media literacy offers prospective teachers new opportunities to succeed and improve school performance. The Canadian, British, Australian, American colleges and universities train teachers in media education. They are acquainted with the media education theory and practice, modern technologies of media education on the system of «key concepts», possibilities of using digital technologies in the process of media education of students. The National Media Education Associations offer vocational courses and media workshops for teachers. One of the most popular ways to improve the qualification and self-education of teachers is MOOC (Futurelearn, Coursera, EdX).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".