Predicting Student Dropouts in Higher Education Using Supervised Classification Algorithms

被引：8

作者：

Serra, Antonella ^{[1
]}

Perchinunno, Paola ^{[2
]}

Bilancia, Massimo ^{[1
]}

机构：

[1] Ion Dept Legal & Econ Syst Mediterranean Soc Envi, Via Lago Maggiore Angolo,Via Ancona, I-74121 Taranto, Italy

[2] Dept Business & Law Studies DEMDI, Largo Abbazia Santa Scolastica 53, I-70124 Bari, Italy

来源：

COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2018, PT III | 2018年 / 10962卷

关键词：

Student dropouts; Higher education; machine learning; Supervised classification;

D O I：

10.1007/978-3-319-95168-3_2

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The aim of this paper is to predict, on a purely algorithmic basis, students who are at risk of dropping out of university. Data used in this study originated from the University of Bari Aldo Moro, during 2013-16, and were provided by the Osservatorio Studenti-Didattica of Miur-Cineca. Data analysis is based solely on the information set available, for each student, inside the university information system. Predictions of individual dropouts have been carried out by means of suitable Machine Learning techniques, known as supervised classification algorithms.

引用

页码：18 / 33

页数：16

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