(untitled) (Innovations in Science and Technologies, 2026)
Distance education has scaled faster than its quality systems can observe it, and nowhere is this clearer than in student persistence: credit-bearing online programmes routinely report attrition several times higher than comparable on-campus cohorts, while open online courses complete at a fraction of their enrolment. Yet most institutions still discover that a student is in trouble only when end-of-term grades arrive, far too late to intervene. This paper proposes a keyperformance- indicator framework for predictive quality monitoring in distance education and situates it within Uzbekistan’s digital-economy agenda. Drawing on the learning-analytics and earlywarning- system literature, we define a compact set of leading indicators, organise them into a monitoring dashboard, and specify a predictive early-warning pipeline that turns learningmanagement- system behavioural data into timely, actionable risk signals. We analyse how predictive performance improves as behavioural data accumulates over a term, map programme health against target bands, and link each indicator to a specific intervention. We argue that predictive, KPI-based quality monitoring is the natural next layer above an interoperable data foundation, and that it offers emerging economies a practical route to credible, data-driven distance education aligned with international quality expectations.