Optimizing education resource allocation using grey model forecasting of school age populations.

Area of Science:

• Demography
• Educational Planning
• Data Science

Background:

• Accurate forecasting of school-aged populations is crucial for effective educational resource allocation.
• Traditional forecasting models struggle with data volatility influenced by policy and environmental factors.
• Grey system theory provides a framework for modeling uncertain systems, but requires enhancement for complex population dynamics.

Purpose of the Study:

• To enhance the Grey Model (GM) (1,1) for more accurate school-aged population forecasting.
• To develop an integrated predictive framework capturing population dynamics amidst socioeconomic shifts.
• To inform data-driven strategies for optimizing educational resource allocation.

Main Methods:

• Optimized school-aged population data using a buffering operator to minimize policy and environmental interference.
• Integrated Locally Weighted Linear Regression to refine grey number calculations, improving fit for volatile data.
• Developed an enhanced GM(1,1) framework combining grey system theory with local regression techniques.

Main Results:

• The enhanced GM(1,1) model demonstrated significantly improved predictive accuracy compared to the traditional GM(1,1) model.
• Residuals were substantially reduced, e.g., from -14.462 to 0.399 in 2018, indicating superior trend-fitting.
• The model effectively captured evolving school-aged population trends during China's socioeconomic shifts (2013-2020).

Conclusions:

• The enhanced GM(1,1) model offers a more precise tool for school-aged population prediction.
• Findings support the development of scientifically grounded strategies for educational resource allocation.
• The study presents a novel methodological approach with theoretical and practical relevance for population forecasting and educational planning.