Standards of teacher competencies in Serbia: Comparative analysis with selected countries
Bibliographic record
Abstract
Many countries have embarked on creating standards of teacher competencies with the ultimate goal of improving teaching in their schools. The aim of this paper is to compare Standards of teacher competencies in Serbia with those in countries/regions which perform well on PISA (Australia, Singapore, Ontario, Estonia, the Netherlands and Slovenia) in order to highlight important similarities and differences which potentially account for teacher quality in those countries, as well as inform policymakers in Serbia on how to reformulate standards of teacher competencies and, consequently, improve teacher quality and pupil outcomes. The criteria on which sets of standards in different countries are compared are: development of standards (who and how developed the standards), content of standards (subject knowledge, didactics, etc.), differentiation of standards (existence of separate sets of competencies for novice teachers, experienced teachers, etc.), purpose of standards (teacher certification, performance monitoring, career progression, accreditation of teacher education providers, etc.), and context in which the standards operate (whether they are a part of a larger framework of standards and educational practices or not). Several important differences exist between the Standards of teacher competencies in Serbia and selected countries, the greatest being the much higher level of utilisation of standards in various segments of teacher professional lives in those countries than in Serbia. Recommendations for the improvement of standards of teacher competencies in Serbia are drawn.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.002 | 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".