The item response theory model for an ai-based adaptive learning system

Cui, W; Xue, Z; Shen, J; Sun, G; Li, J

The item response theory model for an ai-based adaptive learning system

Cui, W Xue, Z Shen, J

Sun, G Li, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 18th International Conference on Information Technology Based Higher Education and Training, ITHET 2019, 2019
Issue Date:: 2019-09-01

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Field	Value	Language
dc.contributor.author	Cui, W
dc.contributor.author	Xue, Z
dc.contributor.author	Shen, J https://orcid.org/0000-0002-9403-7140
dc.contributor.author	Sun, G
dc.contributor.author	Li, J
dc.date	2019-09-26
dc.date.accessioned	2020-06-21T04:32:06Z
dc.date.available	2020-06-21T04:32:06Z
dc.date.issued	2019-09-01
dc.identifier.citation	2019 18th International Conference on Information Technology Based Higher Education and Training, ITHET 2019, 2019
dc.identifier.isbn	9781728124643
dc.identifier.issn	2380-1603
dc.identifier.uri	http://hdl.handle.net/10453/141602
dc.description.abstract	© 2019 IEEE. Item characteristics (e.g. item difficulty), and students' latent traits (e.g. student ability) are essential in a personalized learning system. In such system, items of different characteristics need to be recommended according to students' latent traits. The item response theory can fulfill such requirements. In this paper, we present a detailed description of the student ability and the item difficulty measurement based on an item response theory model. The model is evaluated using real data regarding the math and the English curriculum gathered from students' learning processes during the usage of the Squirrel AI Learning System. The data is regarding the math and the English curriculum. It is proved that the difficulty evaluated by teachers minus the difficulty estimated from the data approximately follows a normal distribution. The mean value of students' abilities approaches 0.50 as the numbers of their attempts on questions increase.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 18th International Conference on Information Technology Based Higher Education and Training, ITHET 2019
dc.relation.ispartof	International Conference on Information Technology Based Higher Education and Training
dc.relation.isbasedon	10.1109/ITHET46829.2019.8937383
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	The item response theory model for an ai-based adaptive learning system
dc.type	Conference Proceeding
utslib.location.activity	Magdeburg, Germany
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 Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-21T04:31:59Z
pubs.finish-date	2019-09-27
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-09-26
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Item characteristics (e.g. item difficulty), and students' latent traits (e.g. student ability) are essential in a personalized learning system. In such system, items of different characteristics need to be recommended according to students' latent traits. The item response theory can fulfill such requirements. In this paper, we present a detailed description of the student ability and the item difficulty measurement based on an item response theory model. The model is evaluated using real data regarding the math and the English curriculum gathered from students' learning processes during the usage of the Squirrel AI Learning System. The data is regarding the math and the English curriculum. It is proved that the difficulty evaluated by teachers minus the difficulty estimated from the data approximately follows a normal distribution. The mean value of students' abilities approaches 0.50 as the numbers of their attempts on questions increase.

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http://hdl.handle.net/10453/141602