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Title: Identifying Risky Dropout Student Profiles using Machine Learning Models
Authors: Prite, Shramin
Gonçalves, Teresa
Rato, Luis
Keywords: Machine Learning
Data Mining
Educational Data
Random Forest
Support Vector Machines
Issue Date: 2020
Citation: Sharmin Prite, Teresa Gonçalves, and Luı́s Rato. Identifying Risky Dropout Student Profiles using Machine Learning Models. In Proceedings of the 26th Portuguese Confe- rence on Pattern Recognition, RECPAD 2020, 2020.
Abstract: Student dropout prediction is essential to measure the success of an educa- tion institute system. This paper focuses on identifying the dropout risk at the University of Évora based on student’s academic performance. Educa- tional data was collected from four different programs, from the academic years of 2006/2007 until 2018/2019. After gathering the raw data, some data pre-processing was done aiming to build a dataset capable of being used by Machine Learning algorithms. Decision trees, Naïve Bayes, Sup- port Vector Machines and Random Forests were evaluated, with the best model reaching an accuracy of around 96% when distinguishing between risky dropout and non-dropout students.
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

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