An Early Warning Model for School Dropout: a Case Study in E-learning Class
Author(s) -
Felipe Braz,
Fernanda Campos,
Victor Ströele,
Mário A. R. Dantas
Publication year - 2019
Publication title -
anais do xxx simpósio brasileiro de informática na educação (sbie 2019)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/cbie.sbie.2019.1441
Subject(s) - disengagement theory , dropout (neural networks) , drop out , warning system , class (philosophy) , computer science , early warning system , order (exchange) , mathematics education , machine learning , artificial intelligence , psychology , telecommunications , gerontology , medicine , finance , economics , demographic economics
Dropping out of school is a real challenge for educational specialists. Considering distance education classes, we have to deal with a huge number of students’ disengagement with social and economic consequences. In order to solve the early drop out problem, this paper proposes the use of an Early Warning System capable of predicting the disengagement of students along the class and notify teachers about this behavior, enabling them to intervene in an effective way and make student’s success possible. In order to evaluate our proposal, we carried out a case study which showed the feasibility of the proposal and the use of its technologies. The results pointed out a significant increase of gain in accuracy along the course, reaching 93% of precision at the end.
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