<![CDATA[Purpose — This study proposes and evaluates an integrated virtual laboratory learning framework that combines virtual laboratories, Learning Management Systems (LMS), and learning analytics to support structured learning in vocational computer network engineering education. The framework is organized into five instructional phases (Highlight–Analyze–Integrate–Do–Assess) to facilitate coherent learning progression and data-informed feedback. Design/methods/approach — An early-stage (single-cycle) Design-Based Research (DBR) approach was employed using a quasi-experimental pre-test–post-test design involving 60 vocational high school students. Data were collected through cognitive achievement tests, practical performance assessments, classroom observations, and LMS-based learning analytics logs. Data analysis included normalized gain (N-gain), paired-sample t-tests, effect size (Cohen’s d), and descriptive and correlational analyses. Findings — The results indicate that the proposed framework is associated with improved cognitive achievement, with mean scores increasing from 58.3 to 82.7 (N-gain = 0.57). A statistically significant difference was found between pre-test and post-test scores (p < 0.001), with a large effect size (d = 1.34). In addition, 90% of students successfully completed virtual laboratory tasks. Learning analytics further revealed reduced error frequency, increased task efficiency, and more consistent engagement patterns over time. Research implications/limitations — Significant correlations were found between learning behaviors and outcomes, particularly task completion rate (r = 0.52). However, due to the absence of a control group, the single-institution context, and the early-stage DBR design, the findings should be interpreted as indicative rather than causal, limiting generalizability. Originality/value — This study contributes by demonstrating how virtual laboratories, LMS, and learning analytics can be systematically aligned within a structured instructional framework in vocational education. The study emphasizes pedagogical sequencing and the integration of behavioral analytics into the learning process, providing initial empirical support for technology-enhanced instructional design that warrants further validation through multi-site and longitudinal studies.]]>
