Technologies of Organizing Prospective Teachers’ Practical Training on the Basis of Competence Approach
Bibliographic record
Abstract
The aim of the article is to determine efficient educational technologies providing the formation of prospective teachers’ professional competencies during their practical training at school. In the conditions of implementing competence approach into the system of teacher training the issues of prospective teachers’ personality, their teaching skills, and the problem of undergraduates’ professional self-determination are of great importance. The article presents the results of the research where much attention is given to assessment of students’ professional activities and the criteria of this assessment. The research included comparing the results of the students’ self-evaluation and the experts’ evaluation of the students’ competence development level. Students’ practical training at school reveals some problems that can be eliminated by using competency-based methods and technologies. The process of students’ practical training at school presupposes proper planning of the activities, analyzing competencies formation, monitoring students’ learning achievements. The content of the article can be used by school teachers, moderators, university professors supervising students’ practical training at school.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".