超越健康:对索马里学校成绩的结构性障碍的机器学习分析
Jibril Abdikadir Ali1, Mukhtaar Axmed Cumar1, Mustafe Khadar Abdi2
1Faculty of Education, School of Postgraduate Studies and Research (SPGSR), Amoud University, Borama, Somaliland.
PloS one
|October 16, 2025
概括
索马里教育排斥主要是由年龄,地区和游牧生活方式驱动的,而不仅仅是健康或贫困. 有针对性的,针对特定年龄的策略对于改善上学率至关重要.
科学领域:
- 公共卫生 公共卫生
- 教育政策 教育政策
- 机器学习应用 机器学习应用
背景情况:
- 像索马里这样的脆弱国家面临着关键的,但不太了解的,健康状况不佳和教育排斥之间的联系.
- 识别学校上学的关键障碍对于有效的政策干预至关重要.
研究的目的:
- 通过一种新的机器学习方法,识别和排名索马里学校上学最重要的障碍.
- 分析影响教育排斥的人口,健康和社会经济因素之间的相互作用.
主要方法:
- 来自2022年索马里综合家庭预算调查 (SIHBS) 的10511名6-18岁儿童的全国代表性数据的分析.
- 采用十个监督机器学习模型来预测学校上学率,其中Random Forest是表现最好的 (AUC=0.86).
主要成果:
- 结构和人口因素,包括孩子的年龄,地理区域和居住类型,是不出席的主要预测因素.
- 不出席的风险随着年龄的增长而显著增加 (6-10岁的孩子有6%,15-18岁的孩子有25%).
- 地理区域 (例如,中沙贝尔有30.5%的非出席) 和游牧生活方式 (三倍的风险) 是关键的决定因素,超过健康和贫困.
结论:
- 索马里教育排斥的根本原因是孩子生活的地方,他们的年龄,以及他们的家庭的生活方式,而不是个人健康或贫困.
- 政策干预应转向针对特定地区,特定年龄的战略.
- 优先考虑边缘化地区的服务提供,支持游牧和青少年人口,对于解决教育不平等至关重要.
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