Use of Cloud Technologies in the Process of Professional and Linguistic Training of Law Students for the Development of Academic Performance
Bibliographic record
Abstract
The objective of this study was to find out how effective the use of cloud technologies is in the formation and development of critical thinking in future lawyers. An experimental model using cloud technologies was tested in training courses in the special (Civil Law, Fundamentals of Administrative Law) and general (English for Specific Purposes, Business English) subjects of the educational professional programme for training specialists of Specialty 081 “Law”.The method of test control and the method of component analysis were used to diagnose the level of academic performance of students selected for the pedagogical experiment in the training courses. To accomplish the research objectives, the results of the author’s tests (seven control points) performed by students of both groups. IBM SPSS Statistics 25.0.0.1 software was used to analyse the quantitative data. Two-tailed P-value and Student’s t test were calculated for statistical processing of experimental data.The study showed the effectiveness of the use of cloud technologies for the formation and development of critical thinking in future lawyers. The authors conclude that the use of cloud technologies in the professional and linguistic training of lawyers also facilitates feedback, which increases students’ educational motivation and allows for monitoring changes in students’ personality 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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".