Аңдатпа
The digital transformation of education has increased the need for interoperable and machine-interpretable frameworks that can support competency-based teacher professional development in STEM-oriented environments. This study proposes an ontology-driven semantic framework for transparent competency mapping and explainable assessment in digital STEM teacher professional development systems. The model was implemented using the Web Ontology Language in the Protégé environment and validated through competency questions expressed as SPARQL queries. The proposed ontology integrates instructional methods, professional competencies, indicators, assessment tools, and control forms within a unified semantic architecture and enables automated reconstruction of the traceability chain linking pedagogical methods to measurable professional outcomes. In contrast to fragmented competency representations, the proposed framework introduces an integrated semantic traceability mechanism supporting explainable competency assessment and ontology-driven validation of professional development structures. The results show that the populated case-study ontology supports automated competency mapping, semantic correspondence matrix generation, detection of missing links, and consistency validation of the modeled relations. The study contributes an ontology-centered semantic framework for digital teacher professional development systems and demonstrates its practical relevance for learning analytics, competency monitoring, and adaptive teacher training environments.