Implementation of Innovative Educational Technologies in the Training of Specialists in Pedagogy and Psychology (European Experience)
Bibliographic record
Abstract
The modern development of educational services market dictates new rules for the use of technologies in education. The purpose of the article is to analyse the introduction of innovative educational technologies into the system of training specialists in pedagogy and psychology based on available European experience. For its implementation, the methods of comparative analysis, concretisation, and generalisation were used. They facilitated the task of characterising the key features of the organisation of innovative educational space on the example of the activities of modern universities in EU countries. In the results, the general principles of transformations in the training of specialists in pedagogy and psychology were analysed, the definition of innovative technologies in the modern understanding of their use in personnel training is given. The experience of teaching and using innovative technologies in Latvia, Romania, and Germany, France (Sorbonne University, University of Karlsruhe, University of Latvia and others) is summarized. One emphasises on the importance of using this experience, which enables students of higher education learning independently, to accumulate knowledge in a non-traditional way by using information and communication technologies and other innovative methods. Important for future use are projects on improving students' multimodal writing practice skills, developing their research skills using modern media libraries and open access informational didactic materials. In the conclusions, it is determined that outside the EU, the system of training specialists and openness to the perception of reforms need further improvement.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".