The Future of Education
Shaping Future Learning Through Digital Skills Profiling
Emil Frashëri, Fan S. Noli University of Korça (Albania)
Abstract
Understanding the heterogeneity of students’ digital skills is essential for designing effective and targeted institutional policies that support curriculum enhancement in higher education. This study profiles university students based on shared behavioral characteristics related to functional digital skills, using empirical data collected from 599 participants. The research examines the multidimensional and hierarchical nature of digital skills and evaluates how different measurement models influence the quality and interpretability of latent student profiles. Latent Profile Analysis (LPA) was conducted using factor scores derived from two measurement approaches: a traditional Confirmatory Factor Analysis (CFA) model and a bifactor‑Exploratory Structural Equation Modeling (B‑ESEM) framework. Comparative findings demonstrate that LPA based on B‑ESEM factor scores yields clearer, more distinct, and theoretically coherent student profiles. This is attributed to the B‑ESEM model’s capacity to simultaneously capture general digital competence and domain‑specific skill dimensions, providing a richer quantitative and qualitative representation of students’ abilities. The results highlight the importance of adopting multidimensional measurement structures to improve student profiling accuracy, thereby enabling higher education institutions to develop more differentiated, targeted, and evidence‑based interventions for integrating digital skills into the curriculum.
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Keywords |
Digital skills, Latent Profile Analysis (LPA), Bifactor‑ESEM , Multidimensional measurement |
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