A Fuzzy ARTMAP Framework for Predicting Student Dropout in Higher Education

Authors: Murshed, N.

Abstract: A framework for predicting students at risk of dropout in Higher Education Institutions is presented. The approach differs from the existing ones in three aspects. First, the proposed approach is based on an ensemble of three Fuzzy ARTMAPs (FMAPs). Second, the decision is based on three risk levels (Low, Medium, High). Third, the student data include students' personal and academic data, and institutional data. Two ensemble learning methods were evaluated: Random Splits and?k-fold Cross Validation. The data used in this study consisted of 29891 of undergraduate student records of students from 2009 to 2018, of which 28% dropouts. The ensemble was developed with 19952 records, and its performance was assessed with 9939 records. The framework achieved an accuracy of 98.44% of predicting dropout with FAR and FRR errors of 1.4% and 2.0%, respectively; and an accuracy of 99.16% of predicting students of high-risk of dropout with FAR and FRR errors of 0.7% and 1.2%, respectively.

Complete article is available at this IEEE link: A Fuzzy ARTMAP Framework for Predicting Student Dropout in Higher Education | IEEE Conference Publication | IEEE Xplore