From Self-Perception to Reality in Digital Competence: Findings from the DigComp 2.2 Framework

Abstract

The rapid expansion of digital technologies in higher education has revealed persistent discrepancies between self-perception and actual performance in digital skills. Self-assessment of digital competence is notoriously unreliable, often masking critical skill gaps by conflating confidence with actual competence. This study, involving 629 military university students, introduces an alternative methodology designed to overcome the limitations of simple self-perception measures. Results from the DigComp 2.2 framework were used to validate two complementary constructs: (1) the Response Consistency Index (RCI), a promising proxy for metacognitive self-awareness, and (2) the Digital Readiness Index (DRI), a second-order latent model built using Confirmatory Factor Analysis within a Structural Equation Modelling framework that integrates the RCI with perceived competences and objective performance. The findings confirm significant discrepancies between perception and competence, identifying profiles consistent with over- and underconfidence (also known as the Dunning–Kruger effect). Additionally, Problem Solving and Information and Data Literacy are identified as the most deficient domains—a weakness observed even in the highest-performing students. The evidence from this study strengthens the idea that digital readiness is not simply the possession of skills, but rather the intersection of competence and accurate self-awareness of that competence. In the era of artificial intelligence, this emphasises the need for precise educational diagnosis and pedagogical intervention to overcome the illusion of digital competence generated by mere self-reports. By providing instructors and instructional designers with diagnostic tools that go beyond declared competence, this approach contributes directly to the advancement of evidence-based, adaptive e-learning practice. The proposed framework supports the development of learning analytics dashboards and pedagogical interventions that respond not only to what students know, but to how accurately they perceive what they know. These validated instruments—the RCI and the DRI—can directly inform the design of adaptive e-learning interventions and personalised digital training pathways in higher education contexts.