预测全球教育不平等与一个等级的信念规则基础模型模型
Aosen Gong1, Wei He2, Gaixia Ge3
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin, China.
Scientific reports
|April 10, 2025
概括
使用可解释层次信任规则基础 (HBRB-I) 模型预测全球教育不平等,有助于政策制定. 这种方法提高了准确性,并保持了公平资源分配的解释性.
科学领域:
- 教育政策 教育政策
- 人工智能的人工智能
- 社会经济发展 社会经济发展
背景情况:
- 全球教育不平等受到社会经济因素的重大影响,特别是在低收入和受冲突影响的地区.
- 准确预测教育不平等对于有效的政策制定和资源分配至关重要.
- 传统的信念规则基础 (BRB) 模型提供了可解释性,但遭受了规则爆炸和缺乏等级结构.
研究的目的:
- 为预测全球教育不平等引入一个可解释的等级信任规则基础 (HBRB-I) 模型.
- 解决传统BRB的局限性,包括规则爆炸和参数偏离专家知识.
- 为了提高预测准确度,同时保持政策应用的模型解释性.
主要方法:
- 在HBRB-I模型中使用多层树结构 (MTS) 进行自我组织的层次结构.
- 开发了一种新的优化方案,以提高预测准确性和保持模型可解释性.
- 应用HBRB-I模型来预测全球教育不平等.
主要成果:
- 该HBRB-I模型在预测全球教育不平等方面表现出高度准确性和稳定性.
- 该模型的可解释性有助于理解导致教育差异的因素.
- 这些发现为公平和可持续的全球教育资源分配提供了数据驱动的支持.
结论:
- 与传统方法相比,HBRB-I模型为预测教育不平等提供了一种优越的方法.
- 该模型的可解释性使决策者能够制定明智的,前性的教育策略,与可持续发展目标保持一致.
- 这项研究支持全球教育资源的公平分配.
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