The Use of Innovative Pedagogical Technologies for Automation of the Specialists’ Professional Training
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it