A comparative analysis of machine learning techniques for student retention management

By Dursun Delen. Decision Support Systems, 2010

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Comparative analysis of data mining methods for bankruptcy prediction (2012, background): “Support vector machines (SVMs) have been proposed for smaller datasets with highly nonlinear relationships [12,15,21,35,40].”
A data-driven approach to predict the success of bank telemarketing (2014, result): “For instance, SVM provided better results in [6][8], comparable NN and SVM performances were obtained in [5], while DT outperformed NN and SVM in [24].”
Systematic review of research on artificial intelligence applications in higher education – where are the educators? (2019, background): “Readers are therefore encouraged to refer to the literature referenced in the articles that are included in this review (e.g. Delen, 2010 and Umer, Susnjak, Mathrani, & Suriadi, 2017). students in general (e.g., Oztekin, 2016; Raju & Schumacker, 2015).”
Educational data mining: prediction of students' academic performance using machine learning algorithms (2022, background): “However, when the focus is on forecasting, neural networks (Delen, 2010; Vandamme et al., 2007), support vector machines (Huang & Fang, 2013), decision trees (Delen, 2011; Nandeshwar et al., 2011) and random forests (Delen, 2010; Vandamme et al., 2007) available input data.”