A Hierarchical and Multidimensional Approach to Profiling Students’ Digital Skills in Educational Technology

This study investigates university students’ digital competence through a multidimensional and person‑centered analytical framework grounded in bifactor–Exploratory Structural Equation Modeling (B‑ESEM). Digital competence was conceptualized across four functional skill domains and a general competence factor, reflecting contemporary perspectives on technology‑enhanced learning and student readiness. To overcome a common limitation in traditional Latent Profile Analysis (LPA), the conflation of overall competence levels with relative skill configurations, this study employed B‑ESEM–derived factor scores to preserve both hierarchical structure and multidimensionality. Using data from 599 university students, a four‑profile solution emerged, capturing distinct patterns of digital skill strengths and weaknesses. Gender and programming experience were incorporated as covariates to better understand demographic and experiential influences on profile membership. The results highlight meaningful variability in students’ digital skill ecosystems and underscore the value of integrating hierarchical modeling with person‑centered approaches to support targeted digital literacy development. These findings offer actionable insights for designing differentiated instructional strategies and educational technology interventions aligned with learner profiles.