Technologies of Organizing Prospective Teachers’ Practical Training on the Basis of Competence Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it