Purpose: In an era of unprecedented student measurement and emphasis on data-driven educational decision making, the full potential for using data to target resources to students has yet to be realized. This study explores the utility of machine-learning techniques with large-scale administrative data to identify student dropout risk. Research Methods: Using longitudinal student records data from the North Carolina Department of Public Instruction, this article assesses modern prediction techniques, with a focus on tree-based classification methods and support vector machines. These methods incorporate 74 predictors measures from Grades 3 through 8, including academic achievement, behavioral indicators, and socioeconomic and demographic characteristics. Findings: Two of the assessed classification algorithms predict high school graduation and dropping out correctly for more than 90% of an out-of-sample student cohort. Findings reveal a shift toward lower dropout incidence in regions hit hardest by the economic recession of 2008, especially for male students. Implications for Research and Practice: Machine-learning procedures, as demonstrated in this study, offer promise for allowing administrators to reliably identify students at risk of dropping out of school so as to provide targeted, intensive programs at the lowest possible cost.

中文翻译：

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教育管理中的“大数据”：预测辍学风险的应用

目的：在一个前所未有的学生衡量和强调数据驱动的教育决策的时代，使用数据将资源定位给学生的全部潜力尚未实现。本研究探讨了机器学习技术与大规模管理数据的效用，以确定学生辍学风险。研究方法：本文使用来自北卡罗来纳州公共教育部的纵向学生记录数据评估现代预测技术，重点是基于树的分类方法和支持向量机。这些方法结合了 3 到 8 年级的 74 个预测指标，包括学业成绩、行为指标以及社会经济和人口特征。发现：两种评估的分类算法可以正确预测 90% 以上的样本外学生群体的高中毕业和辍学。调查结果显示，在受 2008 年经济衰退打击最严重的地区，尤其是男学生，辍学率正在下降。对研究和实践的影响：如本研究所示，机器学习程序有望让管理员可靠地识别有辍学风险的学生，从而以尽可能低的成本提供有针对性的强化课程。