Analyzing technology adoption dynamics in adult education systems through predictive modeling of learner participation and retention

The rapid digitization of education has transformed adult learning environments, with institutions increasingly deploying learning management systems, mobile learning platforms, and analytics-driven instructional tools to improve accessibility and flexibility. Despite these advancements, adult education programs continue to experience inconsistent technology adoption, uneven learner engagement, and significant dropout rates. Adult learners differ from traditional students in that they often balance education with employment, family responsibilities, and varying levels of digital literacy, which can influence their interaction with educational technologies. Consequently, understanding the determinants of technology adoption and sustained participation within adult education systems has become a critical research challenge. This study investigates technology adoption dynamics in adult education through predictive modeling of learner participation and retention patterns within digital learning environments. Using learner interaction logs, course engagement metrics, and demographic variables collected from adult education platforms, machine learning models are developed to predict technology usage intensity and the likelihood of learner persistence throughout course completion. The modeling framework evaluates behavioral indicators such as login frequency, content interaction, assignment submission patterns, and peer collaboration signals to identify predictors of retention outcomes. By linking behavioral learning analytics with predictive modeling, the study provides a systematic approach for identifying early risk signals of disengagement and technology abandonment. The findings offer actionable insights for designing adaptive adult education platforms that support sustained learner participation and improved retention outcomes.