Future Transformations in Teacher Education in Serbia from Three Different Angles
Bibliographic record
Abstract
Investment in human capital is becoming an increasingly important issue for the economic growth of any country. Accordingly, the teacher takes on the role of a reformer because he directly influences the formation of young people as future experts. However, unlike technological and thus social changes, educational changes happen slowly because they need to be viewed from the perspective of creators (higher education institution – HEI), collaborators (mentor teacher in schools) and consumers (students – future teachers). Therefore, the goal of this research is oriented towards looking at the directions of future transformations of teacher education in Serbia, precisely from these three angles. A total of 32 respondents participated in the research (10 teachers from HEI, 9 mentor teachers, and 13 future teachers and beginning teachers-mentee). A questionnaire consisting of 10 questions was used to obtain the results. The results show that all respondents identified the main challenges in the current system of education for future teachers as related to motivation for the teaching profession, a clear education strategy for the coming period, and social status. Key areas that need to be transformed in the next decade are related to increasing mentee competencies required by the complexity of the 21 st century classroom. More specifically, respondents stated that the areas should not undergo a complete transformation, but rather that there should be an increased focus on the specific demands of modern society. Accordingly, the focus should be on student-centered approaches, inclusive, enhanced by technology, with an emphasis on interdisciplinary. The research identified a number of challenges that should be considered before embarking on the transformation of future teacher education. Some of the challenges are: insufficient investment in educational infrastructure, discrepancies in curricula, and sluggishness in following modern trends and innovations. Therefore, the first step proposed is defining professional standards and qualification standards that would be based on the empirical results of all previous reforms, in order to take advantage of the inertia of the system to systematically introduce changes and systematically prepare teachers for better and more inclusive support for mentee.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".