Predicting global educational inequality with a hierarchical belief rule base model

Area of Science:

• Education Policy
• Artificial Intelligence
• Socio-economic Development

Background:

• Global educational inequality is significantly impacted by socio-economic factors, particularly in low-income and conflict-affected areas.
• Accurate prediction of educational inequality is crucial for effective policy-making and resource allocation.
• Traditional Belief Rule Base (BRB) models offer interpretability but suffer from rule explosion and lack hierarchical structure.

Purpose of the Study:

• To introduce an interpretable hierarchical confidence rule base (HBRB-I) model for predicting global educational inequality.
• To address the limitations of traditional BRBs, including rule explosion and parameter deviation from expert knowledge.
• To enhance prediction accuracy while maintaining model interpretability for policy applications.

Main Methods:

• Utilized a multilayer tree structure (MTS) for self-organized hierarchical construction within the HBRB-I model.
• Developed a novel optimization scheme to improve prediction accuracy and preserve model interpretability.
• Applied the HBRB-I model to predict global educational inequality.

Main Results:

• The HBRB-I model demonstrated high accuracy and robustness in predicting global educational inequality.
• The model's interpretability facilitates understanding the factors contributing to educational disparities.
• The findings provide data-driven support for equitable and sustainable global education resource distribution.

Conclusions:

• The HBRB-I model offers a superior approach to predicting educational inequality compared to traditional methods.
• The model's interpretability empowers policymakers to develop informed, forward-looking education strategies aligned with sustainable development goals.
• This research supports the equitable distribution of global educational resources.