Student Dropout Prediction in MOOC using Machine Learning Algorithms

被引：2

作者：

Magalhaes, Elias B. M. ^{[1
]}

Santos, Giovanni A. ^{[2
]}

Molina Junior, Francisco Carlos D. ^{[3
]}

da Costa, Joao Paulo J. ^{[4
]}

de Mendonca, Fabio L. L. ^{[2
]}

de Sousa Junior, Rafael T. ^{[2
]}

机构：

[1] Univ Brasilia UnB, Fac Gama FGA, Brasilia, DF, Brazil

[2] Univ Brasilia UnB, Dept Elect Engn, Brasilia, DF, Brazil

[3] Natl Sch Publ Adm Enap, Brasilia, DF, Brazil

[4] Hamm Lippstadt Univ Appl Sci HSHL, Lippstadt, Germany

来源：

2021 WORKSHOP ON COMMUNICATION NETWORKS AND POWER SYSTEMS (WCNPS) | 2021年

关键词：

Student dropout prediction; machine learning; MOOC;

D O I：

10.1109/WCNPS53648.2021.9626227

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Students' high dropout rate is a common problem on Massive Open Online Course (MOOC) platforms. Therefore, minimizing this problem requires identifying as soon as possible students who have a high probability of dropping out of the course. In this paper, we propose an approach to predict student dropout in the Virtual School of Government (EV.G), elaborating a model that can predict the students' situation at the end of the course based on their performance in the first days and information about their access to the online platform. The processing of the data used was performed with subsequent application of automatic learning techniques, using historical data from EV.G.

引用

页数：6

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