Comparing Machine Learning Algorithms to Predict Topic Keywords of Student Comments

Liu, F; Huang, X; Huang, W

Comparing Machine Learning Algorithms to Predict Topic Keywords of Student Comments

Liu, F Huang, X Huang, W

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12341 LNCS, pp. 178-183
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Liu, F
dc.contributor.author	Huang, X
dc.contributor.author	Huang, W https://orcid.org/0000-0002-5190-7839
dc.date.accessioned	2021-04-01T06:45:04Z
dc.date.available	2021-04-01T06:45:04Z
dc.date.issued	2020-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020, 12341 LNCS, pp. 178-183
dc.identifier.isbn	9783030608156
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/147795
dc.description.abstract	Student comments as a kind of online teaching feedback in higher education organizations are becoming important which provides the evidence to improve the quality of teaching and learning. Effectively extracting useful information from the comments is critical. On the other hand, machine learning algorithms have achieved great performance in automatically extracting information and making predictions. This research compared the performance of three statistical machine learning algorithms and two deep learning methods on topic keyword extraction.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-60816-3_20
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Lecture Notes in Computer Science]. The final authenticated version is available online at: https://link.springer.com/chapter/10.1007%2F978-3-030-60816-3_20]”
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Comparing Machine Learning Algorithms to Predict Topic Keywords of Student Comments
dc.type	Conference Proceeding
utslib.citation.volume	12341 LNCS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Transdisciplinary Innovation
utslib.copyright.status	in_progress	*
dc.date.updated	2021-04-01T06:45:02Z
pubs.publication-status	Published
pubs.volume	12341 LNCS

Abstract:

Student comments as a kind of online teaching feedback in higher education organizations are becoming important which provides the evidence to improve the quality of teaching and learning. Effectively extracting useful information from the comments is critical. On the other hand, machine learning algorithms have achieved great performance in automatically extracting information and making predictions. This research compared the performance of three statistical machine learning algorithms and two deep learning methods on topic keyword extraction.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/147795