Knowledge or gaming? Cognitive modelling based on multiple-attempt response

Wu, R; Xu, G; Chen, E; Liu, Q; Ng, W

Knowledge or gaming? Cognitive modelling based on multiple-attempt response

Wu, R Xu, G

Chen, E Liu, Q Ng, W

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Publication Type:: Conference Proceeding
Citation:: 26th International World Wide Web Conference 2017, WWW 2017 Companion, 2019, pp. 321 - 329
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Wu, R	en_US
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663	en_US
dc.contributor.author	Chen, E	en_US
dc.contributor.author	Liu, Q	en_US
dc.contributor.author	Ng, W https://orcid.org/0000-0003-2183-5245	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	26th International World Wide Web Conference 2017, WWW 2017 Companion, 2019, pp. 321 - 329	en_US
dc.identifier.isbn	9781450349147	en_US
dc.identifier.uri	http://hdl.handle.net/10453/105700
dc.description.abstract	© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Recent decades have witnessed the rapid growth of intelligent tutoring systems (ITS), in which personalized adaptive techniques are successfully employed to improve the learning of each individual student. However, the problem of using cognitive analysis to distill the knowledge and gaming factor from students learning history is still underexplored. To this end, we propose a Knowledge Plus Gaming Response Model (KPGRM) based on multiple-attempt responses. Specifically, we first measure the explicit gaming factor in each multiple-attempt response. Next, we utilize collaborative filtering methods to infer the implicit gaming factor of one-attempt responses. Then we model student learning cognitively by considering both gaming and knowledge factors simultaneously based on a signal detection model. Extensive experiments on two real-world datasets prove that KPGRM can model student learning more effectively as well as obtain a more reasonable analysis.	en_US
dc.relation.ispartof	26th International World Wide Web Conference 2017, WWW 2017 Companion	en_US
dc.relation.isbasedon	10.1145/3041021.3054156	en_US
dc.title	Knowledge or gaming? Cognitive modelling based on multiple-attempt response	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1303 Specialist Studies in Education	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Arts and Social Sciences
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
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. Recent decades have witnessed the rapid growth of intelligent tutoring systems (ITS), in which personalized adaptive techniques are successfully employed to improve the learning of each individual student. However, the problem of using cognitive analysis to distill the knowledge and gaming factor from students learning history is still underexplored. To this end, we propose a Knowledge Plus Gaming Response Model (KPGRM) based on multiple-attempt responses. Specifically, we first measure the explicit gaming factor in each multiple-attempt response. Next, we utilize collaborative filtering methods to infer the implicit gaming factor of one-attempt responses. Then we model student learning cognitively by considering both gaming and knowledge factors simultaneously based on a signal detection model. Extensive experiments on two real-world datasets prove that KPGRM can model student learning more effectively as well as obtain a more reasonable analysis.

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http://hdl.handle.net/10453/105700