Profiling Digital Competency of Prospective Vocational IT Educators Using Generalized DINA Model: A Cognitive Diagnostic Approach

Indonesia faces a significant shortage of digitally competent vocational teachers, yet existing assessment instruments often rely on aggregate scores that fail to diagnose specific cognitive deficits. This study addresses this gap by developing a diagnostic assessment instrument for vocational IT pre-service teachers. The research contribution is the validation of a domain-specific Cognitive Diagnostic Model (CDM) framework that integrates general digital pedagogy with vocational IT technical expertise, enabling precise attribute mastery profiling. The study employed a cross-sectional survey design with a purposive sample of 270 informatics education students. Data were analyzed using a multi-stage psychometric approach, combining Classical Test Theory (CTT), Confirmatory Factor Analysis (CFA), and the Generalized Deterministic Inputs, Noisy "And" Gate (G-DINA) model to determine attribute mastery. The results demonstrated that the 32-item instrument achieved excellent model fit (RMSEA₂=0.011) and outstanding classification accuracy (93.71%). Systematic profiling revealed that female students consistently outperformed males across all dimensions, while a non-linear developmental trajectory was observed with a significant competency decline in the third semester followed by recovery. In conclusion, the G-DINA-based instrument provides a robust diagnostic tool for identifying specific learning needs, suggesting that teacher preparation programs require targeted interventions during critical transition periods to support continuous competency development.