Оцінювання педагогічної компетентності в українських професійно-технічних коледжах: теоретичні основи для воєнного та повоєнного контекстів
Bibliographic record
Abstract
Relevance. In the context of the ongoing full-scale war and the imperative for national recovery, Ukrainian vocational colleges face the critical challenge of significantly enhancing the quality of training for middle-level specialists – technicians, managers, and technologists – essential for rebuilding the economy. The key figure in this process is the lecturer, whose professional competence directly determines the effectiveness of educational outcomes. However, traditional approaches to assessing this competence are often inadequate for the current realities of infrastructure damage, staff and student displacement, psychological strain, and disrupted learning. A paradigm shift is needed: assessment frameworks must move beyond mere evaluation to actively support educator resilience, foster continuous professional growth, and align with European and global standards to ensure the long-term sustainability and international recognition of Ukrainian qualifications. Objective. To theoretically substantiate the principles for assessing pedagogical competence in Ukrainian vocational colleges within wartime and postwar contexts, providing a foundation for developing a responsive, equitable, and developmental evaluation system. Methods. Theoretical analysis of domestic and international scientific literature on pedagogical competence, comparative analysis of international models (EU, Canada, Israel, Kosovo), and synthesis of best practices for competency assessment in crisis-affected educational systems (e.g., DigCompEdu, EQF). Results. The study identifies that the conventional three-dimensional model of pedagogical competence (cognitive-knowledge, practical-activity, personal-social) is insufficient. A fourth, critical dimension – adaptive-resilience competence – is proposed, encompassing the ability to sustain teaching under crisis, respond to trauma, innovate with limited resources, and maintain psychological well-being. Based on international experience and Ukrainian realities, six core theoretical principles for assessment are formulated: 1) Contextualisation – criteria must reflect wartime conditions (e.g., recognizing innovative teaching with limited tech); 2) Holism – evaluation must capture the full spectrum, including resilience, creativity, and social responsibility; 3) Formative Orientation – assessment should primarily serve as a tool for professional development through constructive feedback, not just summative judgment; 4) Flexibility and Adaptability – frameworks must function across hybrid, online, or emergency teaching formats; 5) Equity and Inclusivity – systems must not disadvantage educators with limited access to technology or training; 6) Continuity and Sustainability – short-term assessments must be linked to long-term modernisation and integration into European quality assurance systems. Conclusions. The assessment of pedagogical competence in Ukrainian vocational colleges requires a fundamental transformation. Grounding evaluation in the principles of contextualisation, holism, formative orientation, flexibility, inclusivity, and sustainability can transform assessment from a passive measure into a strategic instrument for rebuilding education. This new paradigm will empower lecturers to become resilient, adaptive, and socially responsible professionals, directly contributing to the recovery of the nation by ensuring that vocational colleges continue to produce skilled, future-ready citizens capable of thriving in a dynamic, post-war society.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".