Who, when, and why: A machine learning approach to prioritizing students at risk of not graduating high school on time.
Several hundred thousand students drop out of high school every year in the United States. Interventions can help those who are falling behind in their educational goals, but given limited resources, such programs must focus on the right students, at the right time, and with the right message. In this paper, we describe an incremental approach that can be used to select and prioritize students who may be at risk of not graduating high school on time, and to suggest what may be the predictors of particular students going off- track. These predictions can then be used to inform targeted interventions for these students, hopefully leading to better outcomes.
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