Characterizing At-Risk Students and measuring treatment effects using machine learning methods

Characterizing At-Risk Students and measuring treatment effects using machine learning methods

A thesis submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, in fulfillment of the requirements for the degree of Doctor of Philosophy, 2020 === Predicting and characterizing students that are at risk of failing is important because it would allow At...

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Bibliographic Details
Main Author:	Smith, Bevan Ian
Format:	Others
Language:	en
Published:	2021
Online Access:	https://hdl.handle.net/10539/31282

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