Knowledge acquisition for learning analytics: Comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin

Liu, DYT; Richards, D; Dawson, P; Froissard, JC; Atif, A

Knowledge acquisition for learning analytics: Comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin

Liu, DYT Richards, D Dawson, P Froissard, JC Atif, A

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9806 LNCS, pp. 183-197
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Liu, DYT
dc.contributor.author	Richards, D
dc.contributor.author	Dawson, P
dc.contributor.author	Froissard, JC
dc.contributor.author	Atif, A https://orcid.org/0000-0003-0381-0343
dc.date.accessioned	2024-08-15T01:55:56Z
dc.date.available	2024-08-15T01:55:56Z
dc.date.issued	2016-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9806 LNCS, pp. 183-197
dc.identifier.isbn	9783319427058
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/180414
dc.description.abstract	One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher-and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model-and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-319-42706-5_14
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Knowledge acquisition for learning analytics: Comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin
dc.type	Conference Proceeding
utslib.citation.volume	9806 LNCS
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	recently_added	*
dc.date.updated	2024-08-15T01:55:55Z
pubs.publication-status	Published
pubs.volume	9806 LNCS

Abstract:

One of the promises of big data in higher education (learning analytics) is being able to accurately identify and assist students who may not be engaging as expected. These expectations, distilled into parameters for learning analytics tools, can be determined by human teacher experts or by algorithms themselves. However, there has been little work done to compare the power of knowledge models acquired from teachers and from algorithms. In the context of an open source learning analytics tool, the Moodle Engagement Analytics Plugin, we examined the ability of teacher-derived models to accurately predict student engagement and performance, compared to models derived from algorithms, as well as hybrid models. Our preliminary findings, reported here, provided evidence for the fallibility and strength of teacher-and algorithm-derived models, respectively, and highlighted the benefits of a hybrid approach to model-and knowledge-generation for learning analytics. A human in the loop solution is therefore suggested as a possible optimal approach.

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http://hdl.handle.net/10453/180414