Comparative Analysis of Forecasting Models for Student Enrollment in Kazakhstan's General Secondary Education System
DOI:
https://doi.org/10.31489/2025ec4/1200Keywords:
student enrollment forecasting, educational planning, demographic modeling, time series analysis, Kazakhstan education system, regional analysisAbstract
Мы сравнили семь моделей прогнозирования численности учащихся в школах Казахстана, используя данные за 2020-2024
годы. Мы протестировали когортные компонентные модели, когортные модели выживаемости, трендовую регрессию с демографическими факторами, линейные
трендовые модели, экспоненциальное сглаживание, многофакторную регрессию и взвешенные скользящие средние по 20 регионам (17
регионам и 3 городам) с примерно 3,9 миллионами учащихся. Точность каждой модели измерялась с помощью средней абсолютной
процентной ошибки (MAPE). Мы обучили модели на данных за 2020-2023 годы и протестировали их на данных за 2024 год. Линейная трендовая
модель показала наилучшие результаты: MAPE составила 0,70 % в масштабах страны и 0,77 % по регионам. Демографические модели оказались менее эффективными
— когортные модели показали плохие результаты на региональном уровне, несмотря на наличие хорошей теоретической основы. Наши прогнозы
на 2025-2027 годы показывают рост числа учащихся в стране с 3,9 млн до 4,2 млн человек, однако темпы роста сильно различаются по
регионам. В крупных городах, таких как Астана, рост составит 24,05%, а в Алматы — 12,81%, в то время как в некоторых регионах рост будет незначительным. Наши
результаты помогают специалистам по планированию образования выбирать правильные методы прогнозирования для разных регионов. Это исследование восполняет пробел
в исследованиях постсоветских стран, где подробные оценки прогнозов найти сложно.
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