论文标题
使用机器学习调查南非和塞拉利昂学校成果之间的相似性和差异
Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning
论文作者
论文摘要
在全球范围内，可用或足够的信息以支持支持学校改善的资源分配是一个关键问题。在本文中，我们将机器学习和教育数据挖掘技术应用于教育大数据，以确定两个非洲国家高中表现的决定因素：南非和塞拉利昂。研究目标是为学校表现建立预测指标，并提取不同社区和学校层面特征的重要性。我们从机器学习方法中部署可解释的指标，例如树模型上的Shap值以及LR的优势比，以提取可以支持政策决策的因素的相互作用。绩效的决定因素在这两个国家 /地区各不相同，因此，政策的影响和资源分配建议不同。
Available or adequate information to inform decision making for resource allocation in support of school improvement is a critical issue globally. In this paper, we apply machine learning and education data mining techniques on education big data to identify determinants of high schools' performance in two African countries: South Africa and Sierra Leone. The research objective is to build predictors for school performance and extract the importance of different community and school-level features. We deploy interpretable metrics from machine learning approaches such as SHAP values on tree models and odds ratios of LR to extract interactions of factors that can support policy decision making. Determinants of performance vary in these two countries, hence different policy implications and resource allocation recommendations.
