A machine learning approach to identify and prioritize college students at risk of dropping out

Abstract

In this paper, we present a student dropout prediction strategy based on the classification with reject option paradigm. In such strategy, our method classifies students into dropout prone or non-dropout prone classes and may also reject classifying students when the algorithm does not provide a reliable prediction. The rejected students are the ones that could be classified into either class, and so are probably the ones with more chances of success when subjected to personalized intervention activities. In the proposed method, the reject zone can be adjusted so that the number of rejected students can meet the available workforce of the educational institution. Our method was tested on a dataset collected from 892 undergraduate students from 2005 to 2016.