Understanding the learning of disabled students: An exploration of machine learning approaches

Homaira, M; Atif, A; Lim, L

Understanding the learning of disabled students: An exploration of machine learning approaches

Homaira, M Atif, A

Lim, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022, pp. 1-6
Issue Date:: 2022-12-20

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Field	Value	Language
dc.contributor.author	Homaira, M
dc.contributor.author	Atif, A https://orcid.org/0000-0003-0381-0343
dc.contributor.author	Lim, L
dc.date	2022-12-18
dc.date.accessioned	2023-02-22T10:42:34Z
dc.date.available	2022-09-27
dc.date.available	2023-02-22T10:42:34Z
dc.date.issued	2022-12-20
dc.identifier.citation	2022, pp. 1-6
dc.identifier.uri	http://hdl.handle.net/10453/166332
dc.description.abstract	Research does not demonstrate how learning analytics can support students with learning disability. It is vital to scrutinise the demographics of students with and without disabilities and their effect on performance. Machine learning algorithms can provide valuable insights by mining this expanding education data. This study aims to analyse the relationship between disabled and nondisabled students' demographic with their scores and the number of attempts to complete a module to develop prediction models. Three models were used: Adaptive Boosting, Random Forest, and K nearest neighbour. The results found that the Adaptive boosting algorithm delivered the highest prediction accuracy.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	IEEE Asia-Pacific Conference on Computer Science and Data Engineering
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Understanding the learning of disabled students: An exploration of machine learning approaches
dc.type	Conference Proceeding
utslib.location.activity	Gold Coast
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
pubs.consider-herdc	true
utslib.copyright.embargo	2025-04-06T00:00:00+1000Z
dc.date.updated	2023-02-22T10:42:33Z
pubs.finish-date	2022-12-20
pubs.publication-status	Accepted
pubs.start-date	2022-12-18

Abstract:

Research does not demonstrate how learning analytics can support students with learning disability. It is vital to scrutinise the demographics of students with and without disabilities and their effect on performance. Machine learning algorithms can provide valuable insights by mining this expanding education data. This study aims to analyse the relationship between disabled and nondisabled students' demographic with their scores and the number of attempts to complete a module to develop prediction models. Three models were used: Adaptive Boosting, Random Forest, and K nearest neighbour. The results found that the Adaptive boosting algorithm delivered the highest prediction accuracy.

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http://hdl.handle.net/10453/166332