Rule mining models for predicting dropout/stopout and switcher at college using satisfaction and SES features
by Nashat T. Al-Jallad; Xu Ning; Mergani A. Khairalla; Mohammed A.A. Al-qaness
International Journal of Management in Education (IJMIE), Vol. 13, No. 2, 2019

Abstract: Predicting students' dropout/stop-out and switch registration aspects at college is one of the important managerial issues that concern the academic institutions. This issue presents a specific challenge due to a large number of factors that can affect the student's decision and the imbalanced nature of the educational data. In this paper, a novel feature extraction method is applied to student satisfaction and socio-economic features during the pre-processing stage to reduce the high dimensionality of the data. Thus, different interpretable data mining approaches, including decision trees and rule induction methods, were examined using actual data of students at the Technical University of Palestine. After resolving imbalanced problem of the students' data, the results showed that the student satisfaction and socio-economic status predictors are important to distinguish different registration aspects. Moreover, the results revealed that J4.8 algorithm achieved best results due to the ability to apply an appropriate trade-off regarding accuracy versus interpretability.

Online publication date: Tue, 05-Mar-2019

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