Comparative Analysis of Forecasting Models for Student Enrollment in Kazakhstan's General Secondary Education System

Abstract

We compared seven forecasting models to predict student enrollment in Kazakhstan’s schools using data from 2020-2024. We tested cohort component models, cohort survival models, trend regression with demographic factors, linear trend models, exponential smoothing, multi-factor regression, and weighted moving averages across 20 regions (17 regions and 3 cities) with about 3.9 million students. We measured how accurate each model was using Mean Absolute Percentage Error (MAPE). We trained models on 2020-2023 data and tested them on 2024 numbers. The linear trend model worked best, with 0.70 % MAPE nationally and 0.77 % MAPE across regions. Demographic models didn’t work as well—cohort models did poorly at the regional level even though they have good theory behind them. Our forecasts for 2025-2027 show national enrollment growing from 3.9 million to 4.2 million students, but growth varies a lot by region. Big cities like Astana will grow 24.05 % and Almaty 12.81 %, while some regions will barely grow at all. Our results help educational planners pick the right forecasting methods for different areas. This study fills a research gap for post-Soviet countries where detailed forecasting evaluations are hard to find.