A theoretical model to predict undergraduate learner attrition using background, individual, and schooling attributes

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dc.contributor.authorMngadi, Noluthando
dc.date.accessioned2021-05-04T10:18:48Z
dc.date.available2021-05-04T10:18:48Z
dc.date.issued2020
dc.descriptionA research report submitted in partial fulfillment of the requirements for the degree of Master of Science in the field of e-Science in the School of Computer Science and Applied Mathematics, University of the Witwatersrand, 2020en_ZA
dc.description.abstractThere is a growing concern around student attrition worldwide, including South African universities. More often than not, the reasons for students not completing their degree in the allocated time frame include academic reasons, socio-pschyo factors, and lack of effective transition from the secondary education system to the tertiary education systems. To overcome these challenges, the tertiary educational institutions endeavor to implement interventions geared toward academic success. One of the challenges, however, is identifying the vulnerable students in a timely manner. This study therefore aims to predict student performance by using a learner attrition model so that the vulnerable students are identified early on in the academic year and are provided support through effective interventions, thereby impacting student success positively. Predictive machine learning methods, such as support vector machines, decision trees, and logistic regression, were trained to deduce the students into four risk-profiles. A random forest outperformed other classifiers in predicting at-risk student profiles with an accuracy of 85%, kappa statistic of 0.7, and an AUC of 0.95. This research argues for a more complex view of predicting vulnerable learners by including the student’s background, individual, and schooling attributesen_ZA
dc.description.librarianCK2021en_ZA
dc.facultyFaculty of Scienceen_ZA
dc.identifier.urihttps://hdl.handle.net/10539/31091
dc.language.isoenen_ZA
dc.schoolSchool of Computer Science and Applied Mathematicsen_ZA
dc.titleA theoretical model to predict undergraduate learner attrition using background, individual, and schooling attributesen_ZA
dc.typeThesisen_ZA
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