The Use of Innovative Pedagogical Technologies for Automation of the Specialists’ Professional Training
Bibliographic record
Abstract
The purpose of this study was to find out how students and teachers perceive the automation of the specialists’ professional training process and the impact factors of perceiving the learning activity of such kind by students and faculty. The experimental model of automated learning was based on an express course in the academic subjects "Roman Private Law" and "Latin (Latin Law Phraseology)". The following methods were used to analyze the quantitative data: Chi-Square statistical method and triangulation. STATA Software was used to process the data. An online Text Analyzer utility was used to process the answers of the focus group respondents to determine the research categories. Automation of the professional training process has a positive impact on education and greatly enhances the opportunities for both teachers and students making it possible to effectively solve the key task of higher education – to teach the student an autonomous learning, as it forms the skills of managing their own time, self-organization, self-motivation, and reflection. Automation of the professional training process through the use of innovative pedagogical technologies brings about a number of new opportunities and advantages, such as: prominence (detailed elaboration of professional processes with different levels), interactivity (ability to control and influence the process), focusing (allows to remove distracting factors, to concentrate on the material). In the proposed automated model, Chatbot can be programmed so that the course participant will not feel the difference between the language of the real person and the machine. Queries that cannot be processed by Chatbot are answered by the course administrator/moderator via email. This model can be adapted and upgraded to teach other professionally oriented theoretical and applied courses. In addition, Chatbot can be used by higher education institutions in managing a university admissions process to provide applicants with information about admission requirements, programmes, specialties, etc.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".