Проблемное и проблемно-ориентированное обучениe (problem-based learning): сравнительный анализ
Bibliographic record
Abstract
The article is devoted to comparative analysis of problem-based learning technology in russian educational practice and at the universities of USA, Canada and West Europe. The criteria comparison are highlidhted such as the learning goals, the models and forms of jount activity organization, the positions of its participants, the problems specifics. The author considers that the classic problem-based learning is built in a leadership model and PBL is realised in a partnership model. In PBL the problem looks like a case maximally closed to a real situation. As the main results of research there has been revealed that PBL is not only technology but the type of the educational practice, all elements of which (a timetable, a content, the forms of sessions, a control, an evaluation) are focused on the formation of students’ ability to learn independently as a metacompetence. The conclusion about inexpediency of complete identification between PBL and the classic problem-based learning is done.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".