Enhancing the Educational Activities of Law Students as One of the Ways to Improve Efficiency and Quality of the Professional Training
Bibliographic record
Abstract
The article urges the problem of enhancing the academic activity of law students as one of the ways of improving the efficiency and quality of professional training. The nature and structure of academic activity of future lawyers are covered. Thus, academic activity is a form of a human activity, which structure is viewed through the prism of mastering a set of knowledge and methods of activity, moral and ethical values, as well as social relations. Academic activity of student youth promotes mastering of basic methods and practical experience of solving professional problems, development of planetary thinking and creativity, that is, it is a professional-oriented activity. In order to enhance the academic activity of law students, the features of its structure were investigated. In particular, the following components were selected: motivational, cognitive and practical.A questionnaire on the topic “Why did I choose the profession of a lawyer?” was conducted. The pedagogical experiment and psychological research based on two methodologies, Ilyina's "Motivation to study in higher educational institutions" and Ayzenko's method of figurative thinking "Find regularity", have been involved.An assessment of the impact done by interactive teaching methods of students, who are studying at the National Aviation University, the specialty "Law", has also been provided.The main ways to solve the problem of intensifying the educational activities of future lawyers have been identified. They include the development of communication, mental processes, motivation to study, active involvement of students in educational activities, and taking responsibility for studying in the higher educational establishment. Problems of interaction between teachers and students while training and increasing students' independence need further development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".