Cyber-Threat Scenario Realism in Digital Simulation: Effects on Educational Technology Students’ Threat Detection and Digital Incident Response Skills

Abstract

Abstract

This study examined the effect of cyber-threat scenario realism, high-realism versus low-realism, within a digital simulation environment on Educational Technology students’ threat detection and digital incident response skills. A quasi-experimental pretest–posttest design with two experimental groups was used. The sample consisted of 100 second-level Educational Technology students, with 50 students assigned to each group. The high-realism group learned through cyber-threat scenarios that included multiple evidence sources, appropriate ambiguity, incident progression, and decision consequences, whereas the low-realism group learned through simpler and more direct scenarios addressing the same skills. The instruments included a core concepts test, two situational tests, two performance rubrics, an interaction analysis log, and a perceived scenario realism scale. Data were analyzed using means, standard deviations, independent-samples t-tests, ANCOVA, gain scores, and effect sizes. The results showed statistically significant posttest differences in favor of the high-realism group across all instruments. The strongest effects appeared in the performance rubrics and interaction analysis log, indicating that high-realism scenarios were particularly effective in developing applied and behavioral dimensions of detection and response. The findings suggest that scenario realism is a meaningful instructional design variable in educational cybersecurity simulation and can help Educational Technology students move from general awareness toward evidence-based and procedurally safe digital incident response.