Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin
Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin
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针头和大海捞针：在威斯康星州建立准确的全州辍学预警系统
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发表时间：
2015
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通讯作者：
J. Knowles
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作者：
J. Knowles
威斯康星州是全国四年制毕业率最高的州之一，但学生群体之间的差距仍然很大。为了解决这个问题，该州创建了威斯康星州辍学预警系统（DEWS），这是一个预测六到九年级学生辍学风险的模型。威斯康星州DEWS在全州范围内使用，目前为超过225，000名学生提供毕业可能性预测。DEWS代表了一种新的基于统计学习的方法来评估学生的非毕业风险的挑战，并为中年级学生提供高度准确的预测，而无需超出强制性的行政数据收集。全国许多管辖区都建立了类似的辍学预警系统。先前的研究表明，在许多情况下，这种系统使用的指标在平衡可能辍学的正确分类和假警报之间的权衡方面做得很差（Bowers等人，2013年）的报告。在这项工作的基础上，DEWS使用受试者操作特征（ROC）指标来确定最佳的统计模型集，以便对个别学生进行预测。本文描述了DEWS方法及其背后的软件，该方法利用了开源统计语言R（R Core Team，2013）。因此，DEWS是一系列灵活的软件模块，可以适应新数据、新算法和新结果变量，不仅可以预测辍学，还可以估算关键预测因子。详细描述了每个模块的设计和实现，以及作为DEWS核心的开源R包EWSTools（Knowles，2014）。
The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use statewide and currently provides predictions on the likelihood of graduation for over 225,000 students. DEWS represents a novel statistical learning based approach to the challenge of assessing the risk of non-graduation for students and provides highly accurate predictions for students in the middle grades without expanding beyond mandated administrative data collections. Similar dropout early warning systems are in place in many jurisdictions across the country. Prior research has shown that in many cases the indicators used by such systems do a poor job of balancing the trade off between correct classification of likely dropouts and false-alarm (Bowers et al., 2013). Building on this work, DEWS uses the receiver-operating characteristic (ROC) metric to identify the best possible set of statistical models for making predictions about individual students. This paper describes the DEWS approach and the software behind it, which leverages the open source statistical language R (R Core Team, 2013). As a result DEWS is a flexible series of software modules that can adapt to new data, new algorithms, and new outcome variables to not only predict dropout, but also impute key predictors as well. The design and implementation of each of these modules is described in detail as well as the open-source R package, EWStools, that serves as the core of DEWS (Knowles, 2014).