Designing for Who Actually Shows Up: A SIGNAL Framework for Online Adult Learners in Cybersecurity and Information Technology

Online adult learners pursuing cybersecurity and information technology credentials represent one of the fastest-growing student populations in American higher education, yet the frameworks institutions use to support their success were not designed for them. This population, disproportionately drawn from the 41.9 million Americans who hold some college credit but no credential, arrives workforce-embedded, time-constrained, and skeptical of institutional systems that previously failed to serve them. Existing persistence models grounded in traditional student integration theory inadequately account for the behavioral patterns, motivational structures, and credential expectations that define this learner. This paper proposes the SIGNAL Framework (Skills-based credential architecture, Integrated AI-informed analytics, Goal-proximate pedagogy, Nontraditional adult learner persistence, Alignment with labor market demand, and Lifelong learning pathway design), a population-specific student success model for online adult learners in cybersecurity and IT. Drawing on learning analytics research, the stackable credentials literature, and the emerging skills-based hiring landscape, SIGNAL addresses the structural mismatch between how institutions measure and support persistence and how this population actually learns. Institutional implications and a targeted research agenda are discussed.