Learning analytics and predictive quality monitoring in distance education: a kpi-based framework for digital economy readiness

Yakhshiboyev , Rustam

Innovations in Science and Technologies · 2026-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarYakhshiboyev , Rustam
JurnalInnovations in Science and Technologies
NashriyotТошкент давлат иқтисодиёт университети
ISSN3030-3451
Nashr sanasi2026-06-10
Jild3
Son6
Betlar27-33
TilIngliz
Ilmiy sohaIqtisodiyot
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

learning analytics, predictive quality monitoring, early-warning systems, key performance indicators, student retention, distance education, digital economy, Uzbekistan

Ilmiy soha

Iqtisodiyot

Iqtibos keltirish

APAYakhshiboyev , Rustam (2026). LEARNING ANALYTICS AND PREDICTIVE QUALITY MONITORING IN DISTANCE EDUCATION: A KPI-BASED FRAMEWORK FOR DIGITAL ECONOMY READINESS. Innovations in Science and Technologies, 3(6), 27-33. https://innoist.uz/index.php/ist/article/view/1577
GOST (OAK)Yakhshiboyev , Rustam LEARNING ANALYTICS AND PREDICTIVE QUALITY MONITORING IN DISTANCE EDUCATION: A KPI-BASED FRAMEWORK FOR DIGITAL ECONOMY READINESS // Innovations in Science and Technologies. – 2026. – T. 3, № 6. – P. 27-33.
MLAYakhshiboyev , Rustam. “LEARNING ANALYTICS AND PREDICTIVE QUALITY MONITORING IN DISTANCE EDUCATION: A KPI-BASED FRAMEWORK FOR DIGITAL ECONOMY READINESS.” Innovations in Science and Technologies, 3(6), 27-33, 2026. https://innoist.uz/index.php/ist/article/view/1577
ChicagoYakhshiboyev , Rustam. “LEARNING ANALYTICS AND PREDICTIVE QUALITY MONITORING IN DISTANCE EDUCATION: A KPI-BASED FRAMEWORK FOR DIGITAL ECONOMY READINESS.” Innovations in Science and Technologies, 3(6), 27-33 (2026). https://innoist.uz/index.php/ist/article/view/1577
HarvardYakhshiboyev , Rustam (2026) ‘LEARNING ANALYTICS AND PREDICTIVE QUALITY MONITORING IN DISTANCE EDUCATION: A KPI-BASED FRAMEWORK FOR DIGITAL ECONOMY READINESS’, Innovations in Science and Technologies, 3(6), 27-33. https://innoist.uz/index.php/ist/article/view/1577
BibTeX
@article{yakhshiboyevrustam2026,
  title = {LEARNING ANALYTICS AND PREDICTIVE QUALITY MONITORING IN DISTANCE EDUCATION: A KPI-BASED FRAMEWORK FOR DIGITAL ECONOMY READINESS},
  author = {Yakhshiboyev , Rustam},
  journal = {Innovations in Science and Technologies},
  year = {2026},
  volume = {3},
  number = {6},
  pages = {27-33},
  url = {https://innoist.uz/index.php/ist/article/view/1577}
}

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