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 Citation (Scopus)

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
StatePublished - 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

Fingerprint

Individual Differences
Cohesion
Network Analysis
Electric network analysis
Linear Model
Students
Predict
Modeling
Class

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

Modeling math success using cohesion network analysis. / Crossley, Scott A.; Sirbu, Maria Dorinela; Dascalu, Mihai; Barnes, Tiffany; Lynch, Collin F.; McNamara, Danielle.

Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Springer Verlag, 2018. p. 63-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10948 LNAI).

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

Crossley, SA, Sirbu, MD, Dascalu, M, Barnes, T, Lynch, CF & McNamara, D 2018, Modeling math success using cohesion network analysis. in Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10948 LNAI, Springer Verlag, pp. 63-67, 19th International Conference on Artificial Intelligence in Education, AIED 2018, London, United Kingdom, 6/27/18. https://doi.org/10.1007/978-3-319-93846-2_12
Crossley SA, Sirbu MD, Dascalu M, Barnes T, Lynch CF, McNamara D. Modeling math success using cohesion network analysis. In Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Springer Verlag. 2018. p. 63-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-93846-2_12
Crossley, Scott A. ; Sirbu, Maria Dorinela ; Dascalu, Mihai ; Barnes, Tiffany ; Lynch, Collin F. ; McNamara, Danielle. / Modeling math success using cohesion network analysis. Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Springer Verlag, 2018. pp. 63-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{e07db31cef7f4280bf6394f566792d01,
title = "Modeling math success using cohesion network analysis",
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.",
keywords = "Cohesion network analysis, Math success, NLP, Online learning",
author = "Crossley, {Scott A.} and Sirbu, {Maria Dorinela} and Mihai Dascalu and Tiffany Barnes and Lynch, {Collin F.} and Danielle McNamara",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-93846-2_12",
language = "English (US)",
isbn = "9783319938455",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "63--67",
booktitle = "Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings",

}

TY - GEN

T1 - Modeling math success using cohesion network analysis

AU - Crossley, Scott A.

AU - Sirbu, Maria Dorinela

AU - Dascalu, Mihai

AU - Barnes, Tiffany

AU - Lynch, Collin F.

AU - McNamara, Danielle

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Cohesion network analysis

KW - Math success

KW - NLP

KW - Online learning

UR - http://www.scopus.com/inward/record.url?scp=85049368021&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049368021&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-93846-2_12

DO - 10.1007/978-3-319-93846-2_12

M3 - Conference contribution

SN - 9783319938455

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 63

EP - 67

BT - Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings

PB - Springer Verlag

ER -