Accessibility Aware Employability Analytics Using Workplace Simulation Logic and Person Job Fit Modeling

The transition from education to employment remains a major challenge, particularly for individuals who may require accessibility support during competency assessment and occupational guidance. However, many current approaches remain fragmented because they evaluate soft skills, accessibility conditions, and occupational requirements as separate dimensions. This study presents an accessibility-aware computational proof of concept for employability analytics using workplace simulation logic, derived competency indicators, semantic modeling, clustering, person–job fit estimation, and heuristic multi-objective optimization. The framework integrates open secondary employability data, O*NET-derived occupational descriptors, and simulated accessibility scenarios within a reproducible analytical pipeline. The results show differentiated computational employability profiles, with mean person–job fit values of 0.85, 0.74, and 0.63 for high, medium, and low profiles, respectively. The derived competency indicators showed high internal consistency (α=0.905), although they are interpreted as exploratory proxy dimensions rather than as an exploratory psychometric scale. Principal component analysis indicated a dominant general employability factor, with the first component explaining 75.3% of the variance. The optimization layer produced interpretable heuristic convergence patterns and modeled scenario assignments under predefined validity, accessibility, alignment, and diagnostic criteria. Person–job fit was interpreted under sensitivity scenarios involving alternative competency weights, scalarization parameters, and accessibility assumptions. The study does not include observed participants with disabilities, measured accessibility support use, field simulator interaction records, or longitudinal employment outcomes. Therefore, the term accessibility-aware refers to the computational framework’s design orientation. At the same time, the empirical evidence should be interpreted as a secondary-data-based proof of concept rather than as validation of an inclusive simulator for future users with accessibility needs. The main numerical indicators were: high-profile mean fit = 0.85, medium-profile mean fit = 0.74, low-profile mean fit = 0.63, Cronbach’s alpha = 0.905, first principal component variance = 75.3%, and heuristic iterations = 900.