Learning patterns of university student retention

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摘要

Learning predictors for student retention is very difficult. After reviewing the literature, it is evident that there is considerable room for improvement in the current state of the art. As shown in this paper, improvements are possible if we (a) explore a wide range of learning methods; (b) take care when selecting attributes; (c) assess the efficacy of the learned theory not just by its median performance, but also by the variance in that performance; (d) study the delta of student factors between those who stay and those who are retained. Using these techniques, for the goal of predicting if students will remain for the first three years of an undergraduate degree, the following factors were found to be informative: family background and family’s social-economic status, high school GPA and test scores.

论文关键词:Data mining,Student retention,Predictive modeling,Financial aid

论文评审过程:Available online 15 June 2011.

论文官网地址:https://doi.org/10.1016/j.eswa.2011.05.048