This study presents a reproducible applied IT-based digital verification and analytics system designed to model and measure the development of information-analytical competence through a structured LMS-driven workflow architecture in algorithm-mediated information environments characterized by misinformation, manipulation, and information overload. Professional readiness in such contexts requires systematized source investigation, cross-source corroboration, and evidence-grounded decision modeling supported by traceable digital procedures rather than passive information access. The proposed system operationalizes information-analytical competence as an executable workflow-based construct integrating verification routines (CRAAP/SIFT protocols), structured reasoning artifacts, and analytics-ready performance logs within a controlled platform configuration. The architecture includes digital artifact generation (search logs, argument maps, rubric-scored outputs), standardized CSV export via LMS gradebook and form-response modules, anonymized dataset aggregation using pseudonymized identifiers, and baseline-adjusted statistical modeling. The system was deployed across three universities (N = 448) under a fixed configuration protocol and evaluated using a quasi-experimental pre-test/post-test design with ANCOVA specification in SPSS 27.0 (post-test as dependent variable, group as fixed factor, pre-test as covariate). Results indicate statistically significant competence gains in the Experimental Group compared to a non-workflow configuration (F (1,445) = 31.84, p<.001; d≈0.85), with strongest effects in analytically structured and reflective dimensions. The findings validate the feasibility of a scalable, workflow-driven applied IT framework that integrates LMS-based execution, structured data pipelines, artifact-level traceability, and reproducible learning analytics for competence modeling in digital education systems.