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Title: Applying predictive analytics in identifying students at risk: A case study
Authors: Lourens, Amanda 
Bleazard, David 
Keywords: Students at risk;Predictive learner analytics;Retention of students;Student dropout;Ogistic regression;Decision trees;Naïve Bayes
Issue Date: 2016
Publisher: South African Journal of Higher Education
Abstract: In this article, a case study is presented of an institutional modelling project whereby the most appropriate learning algorithm for the prediction of students dropping out before or in the second year of study was identified and deployed. This second-year dropout model was applied at programme level using pre-university information and first semester data derived from the Higher Education Data Analyzer (HEDA1) management information reporting and decision support environment at the Cape Peninsula University of Technology. An open source platform, namely Konstanz Information Miner (KNIME2), was used to perform the predictive modelling. The results from the model were used in HEDA automatically to recognize students with a high probability of dropping out by the second year of study. Being able to identify such students will enable universities, and in particular programme owners, to implement targeted intervention strategies to assist the students at risk and improve success rates
ISSN: 1753-5913
Appears in Collections:Edu - Journal Articles (DHET subsidised)

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