Modelling math learning on an open access intelligent tutor

David Azcona, Ihan Hsiao, Alan F. Smeaton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper presents a methodology to analyze large amount of students’ learning states on two math courses offered by Global Freshman Academy program at Arizona State University. These two courses utilised ALEKS (Assessment and Learning in Knowledge Spaces) Artificial 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 Tutoring Systems to examine the potential of the embedding representations on students learning.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
EditorsRose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Manolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, Roberto Martinez-Maldonado, H. Ulrich Hoppe
PublisherSpringer Verlag
Pages36-40
Number of pages5
ISBN (Print)9783319938455
DOIs
StatePublished - 2018
Event19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom
Duration: Jun 27 2018Jun 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10948 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Artificial Intelligence in Education, AIED 2018
Country/TerritoryUnited Kingdom
CityLondon
Period6/27/186/30/18

Keywords

  • Intelligent tutoring systems
  • MOOC
  • Machine learning
  • Social network analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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