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Modelling math learning on an open access intelligent tutor
Azcona, David; Hsiao, I-Han; Smeaton, Alan F.
This paper presents a methodology to analyze large amount of students’ learning states on two math courses offered by Global Fresh- man Academy program at Arizona State University. These two courses utilised ALEKS (Assessment and Learning in Knowledge Spaces) Arti- ficial Intelligence technology to facilitate massive open online learning. We explore social network analysis and unsupervised learning approaches (such as probabilistic graphical models) on these type of Intelligent Tu- toring Systems to examine the potential of the embedding representa- tions on students learning.
Keyword(s): Education; Artificial intelligence; Educational technology; Machine Learning; Intelligent Tutoring Systems; Social Network Analysis; MOOC
Publication Date:
2018
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Azcona, David ORCID: 0000-0003-3693-7906 <https://orcid.org/0000-0003-3693-7906>, Hsiao, I-Han ORCID: 0000-0002-1888-3951 <https://orcid.org/0000-0002-1888-3951> and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 <https://orcid.org/0000-0003-1028-8389> (2018) Modelling math learning on an open access intelligent tutor. In: The 19th International Conference on Artificial Intelligence in Education, June 27 - 30, 2018, London, UK. ISBN 978-3-319-93846-2
Publisher(s): Springer International Publishing
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/22448/1/David_Azcona_AIED_2018.pdf,
https://doi.org/10.1007/978-3-319-93846-2_7
First Indexed: 2018-07-04 06:08:31 Last Updated: 2019-02-12 06:06:39