MODERN METHODS OF RESEARCH-BASED TEACHING AND LEARNING: FOREIGN EXPERIENCE
Bibliographic record
Abstract
The article describes theoretical foundations of research-based teaching and learning, their role in shaping a research competence of students, their critical and creative thinking. It has been pointed out that research-based teaching and learning is one of the main trends of modern European education, enshrined in the European Higher Education Area (EHEA) strategic and analytical documents. The model of scientific researches integration in the educational process of a university is considered, which is constructed using 2 criterias: a degree of students perception of scientific problems and a degree of students involvement in a scientific research work. The experience of research-based teaching and learning, from universities of different countries (Japan, UK, Australia, New Zealand, USA, Canada) is analyzed and classified according to the methods of educating. It is noted that the most effective methods for a development of the students’ researches competence are active methods, which stimulate active mental and practical performance during an acquisition of educational material. Students participate in a process of cognition; they exchange information, analyze it, consider alternative thoughts, participate in a discussion, model situations, evaluate the actions of others and their own behavior, make thoughtful decisions, that is, collectively solve educational and scientific problems, plunging into a real atmosphere of scientific cooperation. Specific attention is paid to such active learning and educating methods as project method, case method, discussions, research method, game techniques, communication with leading scientists, specialty practical activity as well as an introduction of scientific research results into production.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".