Modern Psychological and Teaching Technologies for Implementing the Educational Process in Higher Educational Institutions of Ukraine
Bibliographic record
Abstract
The relevance of the subject under study lies in the use of the latest educational technologies in higher educational institutions of Ukraine. As a consequence, the study focuses on the concept of teaching technology in the psychological and educational literature and on identifying the most optimal teaching programme for institutions of higher education for the implementation of modern innovative technologies. The above listed objectives determine the purpose of this study — to establish and test a curriculum for the implementation of modern psychological and teaching technologies of the educational process in Ukrainian universities. The leading methods included the organisation of experimental research on the development and modelling of the curriculum using the latest technologies. During the establishing and controlling stages of this study, the cross-sectional method was employed to learn the features and regularities of the mental development of higher education students, using the latest psychological and teaching technology in education. The results of the study consider the present-day requirements and demonstrate the necessity of incorporating such technologies as self-development and distance learning. The programme includes recommendations for the most successful implementation of the educational process, guided by the student's personality. The main idea of this programme is “the students are taught by themselves, not by the teacher”. The significance of the results of this study is valuable for conscious students, teachers inspired by their craft, and Ukrainian universities that strive to fill the labour market with prominent specialists, as opposed to graduates with a “plastic diploma”.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".