Modeling math success using cohesion network analysis

Scott A. Crossley, Maria Dorinela Sirbu, Mihai Dascalu, Tiffany Barnes, Collin F. Lynch, Danielle McNamara

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

1 Scopus citations

Abstract

This study examines math success within a blended undergraduate course using a Cohesion Network Analysis (CNA) approach while controlling for individual differences and click-stream variables that may also predict math success. Linear models indicated that math success was related to days spent on the forum and by students who more regularly posted in the online class forum and whose posts generally followed the semanticity of other students.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
PublisherSpringer Verlag
Pages63-67
Number of pages5
ISBN (Print)9783319938455
DOIs
Publication statusPublished - Jan 1 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
CountryUnited Kingdom
CityLondon
Period6/27/186/30/18

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Keywords

  • Cohesion network analysis
  • Math success
  • NLP
  • Online learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Crossley, S. A., Sirbu, M. D., Dascalu, M., Barnes, T., Lynch, C. F., & McNamara, D. (2018). Modeling math success using cohesion network analysis. In Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings (pp. 63-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10948 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93846-2_12