Analyzing Performance Patterns and user Experience in Online Cybersecurity Challenges: Insights from SKRCTF

The field of cybersecurity education has undergone a significant transformation, with the emergence of innovative e-learning platforms that aim to bridge the gap between academic institutions and industry. This study investigates the effectiveness of SKRCTF, a customized Capture-the-Flag platform designed to support cybersecurity skill development, through comprehensive analysis of user performance patterns and learning experiences. Drawing from data collected from 120 participants over a 12-week period, this research employs mixed-methods analysis to examine learning progressions, engagement patterns, and skill development pathways in cybersecurity education. The findings reveal three key insights: 1. the emergence of distinct specialist and generalist learning patterns, challenging traditional assumptions about linear skill progression; 2. the identification of an optimal engagement zone that maximizes learning effectiveness; and 3. the discovery that analytical challenges serve as effective entry points for cybersecurity skill development. The study also uncovers a complex relationship between perceived difficulty and learning outcomes, where higher perceived difficulty often correlates with increased persistence rather than decreased engagement. These insights contribute to both theoretical understanding of cybersecurity skill development and practical platform design considerations. The research suggests that effective cybersecurity education platforms should support multiple learning pathways, implement structured engagement mechanisms, and carefully sequence challenges to optimize skill development. These findings have significant implications for the design of cybersecurity education programs and the development of future security professionals.