HEMIS BIG DATA MA'LUMOTLARI ASOSIDA O'ZBEKISTONDA TA'LIM YO'NALISHLARI BO'YICHA KADRLAR TAYYORLASH TENDENSIYASI: TA'LIM SHAKLLARINING TARKIBIY DEKOMPOZITSIYASI VA SIMPSON PARADOKSI (2023–2026)
Keywords:
Big Data, HEMIS, Simpson's paradox, education forms, full-time education, part-time education, STEM, workforce training, teacher shortage, demographic forecast, gender segregation, human capital.Abstract
This article analyzes workforce training trends in Uzbekistan during
2023–2026 based on HEMIS Big Data (nearly 2 million records) across educational
InnoRes Journal – Vol.2 No.5 (2026) www.innores.uz
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fields, education forms (full-time, part-time, evening, distance), STEM/Non-STEM
composition, bachelor's/master's levels, regions, and gender. Simpson's paradox
(aggregation paradox) was identified: pedagogy (–26%), engineering (–26%), and IT
(–11%) appear to decline overall, but show growth in full-time education by +45%,
+10%, and +11% respectively. The decline is solely due to systemic restriction of
part-time education (–84%). Demographic-pedagogical disproportion analysis
projects an annual teacher shortage of 16,000. Findings are substantiated within
Hanushek and Woessmann's human capital quality theory, Lewis's structural change
model, and Simpson's statistical paradox.
