Влияние современных теорий обучения на трансформацию методической науки в высшей педагогической школе Украины
Bibliographic record
Abstract
In the article the different strategies of teaching of students are analysed in the countries of the globalised world (the USA, Canada, Great Britain), among which are named: constructivism (D. Djonassen, R. Ronning, G. Shrow and others), theory of transformation (R. Boyd, M. Clark, J. Mezirov and others), humanistic and cognitive theory of learning (A. Maslow, J. Miller and others), which are variously explained by a teaching process. It is specified that at the level of private theories and teaching models they complement each other quite often.It is described that at determination of strategies of teaching in the countries of the globalised world, priority is given to: development of motivation to teaching; self-education, self-realisation and self-actualisation of personality; organisation of the process of teaching that eliminates competition; teaching to work in a team; forming of willingness to live and work in difficult and constantly changing world.Based on modern theories of teaching mentioned above, basic changes in methodological science are named at the higher pedagogical school in the preparation of future primary school teachers, which at the focus of attention put competence approach, educational results and methods of their measuring; functions, roles of teacher and students; update of maintenance of leading methodological principles, forms, technologies; variability, interdisciplinarity, antropologisation, informatisation, globalisation
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".