<![CDATA[This study investigated the effectiveness of Scenario-Based Learning (SBL) using Wireshark in improving students’ packet analysis and network disturbance diagnostic skills. The study addressed the gap between theoretical networking instruction and students’ limited ability to interpret real network traffic and troubleshoot practical cases. The key argument is that integrating authentic troubleshooting scenarios with professional packet analysis tools enhances higher-order analytical competence. A quantitative pre-experimental one-group pretest–posttest design was employed. Thirty-two second-year networking students participated in a four-week intervention consisting of structured scenario modules using Wireshark (version 4.x) in a controlled laboratory environment. Data were collected through a 20-item performance-based test measuring conceptual understanding, packet interpretation, and network diagnosis. Statistical analysis included descriptive statistics, Shapiro–Wilk normality testing, paired-sample t-test, and Cohen’s d effect size calculation. The mean score increased from 56.41 (SD = 8.72) in the pretest to 78.63 (SD = 7.95) in the posttest. The paired-sample t-test revealed a significant difference, t(31) = 15.87, p < 0.001. The calculated effect size (Cohen’s d = 2.80) indicated a very large effect. The highest gains were observed in packet interpretation and network diagnosis components. These results confirm that SBL using Wireshark significantly improves analytical and troubleshooting competence. The absence of a control group, limited sample size (N = 32), short intervention duration, and single-institution context may restrict generalizability. Future studies employing randomized or longitudinal designs are recommended. This study provides empirical evidence of integrating scenario-based pedagogy with industry-standard packet analysis tools in networking education. It offers a replicable instructional framework that bridges theory and authentic technical practice.]]>
