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Dropout remains a persistent challenge within high school education. In this paper, we present a case study on automatically detecting whether a student is at-risk of dropout within a diverse school district in Texas. We predict whether a student will drop out in a future school year from data on students’ discipline, attendance, course-taking, and grades, using a logistic regression framework. We discuss the predictive properties of the model, and the features that are predictive of dropout in this context.
Baker et al. (Tue,) studied this question.
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