Early prediction of students at risk in a virtual learning environment using ensemble machine learning techniques

Soobramoney, Ranjin

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Durban University of Technology

Early prediction of students at risk in a virtual learning environment using ensemble machine learning techniques

dc:creator Soobramoney, Ranjin

dc:date2022

Abstract

dc:description

Submitted in fulfillment of the requirements for the Degree of Masters of Information and Communication Technology, Durban University of Technology, Durban, South Africa, 2021.

Author and committee

dc:creator, dc:contributor.*

Author dc:creator: Soobramoney, Ranjin
Contributors dc:contributor: Singh, Alveen

Subjects

dc:subject × 9

Rights

Language dc:language: en

Identifiers

dc:identifier.*

Identifier: https://doi.org/10.51415/10321/4072
OAI identifier oai:identifier: oai:openscholar.dut.ac.za:10321/4072

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Harvested from: Durban University of Technology
Base URL: openscholar.dut.ac.za/server/oai/request
Last updated: 2026-07-27
Source record: OAI-PMH GetRecord

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citation

Soobramoney, Ranjin. Early prediction of students at risk in a virtual learning environment using ensemble machine learning techniques. 2022. https://hdl.handle.net/10321/4072

Early prediction of students at risk in a virtual learning environment using ensemble machine learning techniques

Abstract

Author and committee

Subjects

Rights

Identifiers

Chain of custody

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