Implementation of Quality Control of the Educational Process in Higher Education Institutions Based on Models
Bibliographic record
Abstract
Modern Ukrainian society is in a complex process of transformation in various spheres at the present stage of its historical development, including, in the field of education. The humanitarian development of the country and the world sets new tasks for the national education system, the solution of which is inefficient within the framework of the existing methodology. Currently, the issue of analysing the efficiency of the higher education system and its adaptability in the field of training specialists of various profiles to constant and changing processes in various spheres of the society is of particular relevance. The inconsistency of the scientific-theoretical and scientific-practical level of the quality control model of higher education has determined the problem of the necessity to analyse the existing models in order to improve their individual aspects. The purpose of the academic paper is to study the existing models of quality control of the educational process in higher education and to clarify the practical features of the development of such models from the perspective of participants in the learning process in higher educational institutions. Methodology. In the course of the research, the analytical-bibliographic method was used to study the scientific literature on the issues of quality control of the educational process in higher educational institutions, as well as a questionnaire survey in order to clarify in practice the certain aspects of developing the models for the quality management of education in higher educational institutions. Results. Based on the results of the research, the features of implementing the individual models of quality control of the educational process in higher educational institutions were studied, and the main principles and characteristics that should be taken into account when developing such a model for each individual institution of higher education were clarified.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".