The item response theory model for an AI-based adaptive learning system Cui, Wei, Xue, Zhen, Shen, Jun, Sun, Geng and Li, Jianxin 2019, The item response theory model for an AI-based adaptive learning system, in ITHET 2019 : Proceedings of the 18th International Conference on Information Technology Based Higher Education and Training 2019, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/ITHET46829.2019.8937383. Attached Files Name Description MIMEType Size Downloads Title The item response theory model for an AI-based adaptive learning system Author(s) Cui, Wei Xue, Zhen Shen, Jun Sun, Geng Li, Jianxin orcid.org/0000-0002-9059-330X Conference name Information Technology Based Higher Education and Training. Conference (18th. 2019, Magdeburg, Germany) Conference location Magdeburg, Germany Conference dates 2019/09/26 - 2019/09/27 Title of proceedings ITHET 2019 : Proceedings of the 18th International Conference on Information Technology Based Higher Education and Training 2019 Publication date 2019 Start page 1 End page 6 Total pages 6 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) Item difficulty Ability measurement Item response theory Big data Logistic model Maximum likelihood estimation ISBN 9781728124643 Language eng DOI 10.1109/ITHET46829.2019.8937383 Indigenous content off HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30134267 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Training - Quality check Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 31 Jan 2020, 13:31:25 EST Sat, 01 Feb 2020, 04:00:45 EST Mon, 03 Feb 2020, 07:06:32 EST Mon, 03 Feb 2020, 08:32:47 EST Tue, 04 Feb 2020, 05:05:41 EST Fri, 03 Apr 2020, 13:43:59 EST Fri, 03 Apr 2020, 13:59:32 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 24 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 03 Feb 2020, 08:32:47 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.