Challenges of developing professional practice in vocational education and training - The Kosovo case
Bibliographic record
Abstract
Challenges in vocational education and training (VET) remain among the primary issues confronting Kosovo’s education system. The most critical challenges are structural and institutional, particularly those related to implementing professional practice, enhancing youth employability, and aligning VET with labor market demands. The study employed both quantitative and qualitative methods, conducted across five municipalities, nine schools, and forty partner companies. Participants included 125 students, nine school coordinators responsible for professional practice, and thirty-nine company mentors. Findings indicate that the absence of structured institutional cooperation, the predominance of theoretical content, and inadequate practical infrastructure significantly undermine VET quality. They further highlight the crucial role of trained instructors as a decisive factor for process effectiveness. A comparison with successful European models, particularly the German and Swiss systems, underscores gaps in Kosovo’s context, emphasizing the need for institutionalized partnerships, infrastructure modernization, and curriculum reform. The study recommends developing a national strategy for professional practice, training and certifying instructors, and introducing fiscal incentives for businesses offering placements. These conclusions provide a foundation for sustainable policies and strategic interventions aimed at improving youth employability and fostering long-term economic growth.
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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 | 0.006 | 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 | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".