Interactive Learning as Means of Formation of Future Teachers’ Readiness for Self-education
Bibliographic record
Abstract
The problem under study is relevant due to an increased role of future teachers’ self-education as a factor of his competitiveness and effective self-realization in the conditions of global paradigm of lifelong education and the necessity of designing educational technologies for formation of future teachers’ readiness during his study at the university. The aim of the article is to study the effectiveness of interactive learning technologies in the practice of training future teachers as a means of forming their readiness for professional self-education. Main approaches to the study of this problem are the system and the activity approach, implemented in the process of formation of readiness of the future teachers for professional self-education at a modern university. Analysis of the results presented in this paper suggests that the use of the system application and methodically based combination of a variety of interactive learning technologies in the training of future teachers has a positive impact on the formation of all the components of readiness of future teachers for professional self-education (motivational, value, cognitive, activity, reflexive). The article may be useful for university teachers implementing training of future teachers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".