Constructivist Approach in a Paradigm of Public School Teachers′ Professional Development in Great Britain, Canada, the USA
Bibliographic record
Abstract
Abstract The article dwells on professional development of public school teachers as an inevitable constituent of education systems in the 21st century. In such economically developed countries as Great Britain, Canada and the USA, the problem of preparing teachers to a difficult and responsible task of upbringing and educating future citizens always remains topical. The authors define the following aim and objectives of their research: to conduct analysis of scientific and pedagogical literature and to define the notion of teachers′ “professional development”; to research a place and role of the constructivist approach to professional development of teachers. Some aspects of the problem under research have been studied by foreign and domestic scientists: political, social, cultural and economic aspects of teachers′ professional development (L. Darling-Hammond, M. Tight); elaboration of professional development curricula (C. Pratt); content of teachers′ professional development (N. Dana Fichtman, S. Zepeda); concept-oriented instruction (J. Guthrie); continuing professional development (Ya. Belmaz, A. Kuzminskyi, O. Kuznietsova). The research methodology comprises theoretical (logical, structural and systematic methods, induction and deduction, comparison and compatibility, analysis and synthesis) and applied (observations, questioning and interviewing) methods. The research results have been presented.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".