Exploring Online Course Sociograms Using Cohesion Network Analysis

Maria-Dorinela Sirbu; Mihai Dascalu; Scott A.  Crossley; Danielle S. McNamara; T. Barnes; C.F. Lynch; Stefan Trausan-Matu

doi:10.1007/978-3-319-93846-2_63

Exploring Online Course Sociograms Using Cohesion Network Analysis

Maria-Dorinela Sirbu, Mihai Dascalu, Scott A. Crossley, Danielle S. McNamara, T. Barnes, C.F. Lynch, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

Abstract

Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in online courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students’ behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and to
identify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.

Original language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II
Editors	Rose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Manolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, Roberto Martinez-Maldonado, H. Ulrich Hoppe
Place of Publication	Cham
Publisher	Springer
Pages	337–342
Number of pages	6
Volume	Part II
ISBN (Electronic)	9783319938462
ISBN (Print)	9783319938455
DOIs	https://doi.org/10.1007/978-3-319-93846-2_63
Publication status	Published - 2018
Externally published	Yes
Event	International Conference, AIED 2018 - London, United Kingdom Duration: 27 Jun 2018 → 30 Jun 2018 https://aied2018.utscic.edu.au/

Publication series

Series	Lecture Notes in Computer Science
Volume	10948

Conference

Conference	International Conference, AIED 2018
Abbreviated title	AIED 2018
Country/Territory	United Kingdom
City	London
Period	27/06/18 → 30/06/18
Internet address	https://aied2018.utscic.edu.au/

Keywords

Cohesion Network Analysis
Interaction patterns
Online courses
Participants clustering
Sociograms

Access to Document

10.1007/978-3-319-93846-2_63

Rage: Realising an Applied Gaming Eco-system
Westera, W. (PI), Georgiadis, K. (CoI), Saveski, G. (CoI), van Lankveld, G. (CoI), Bahreini, K. (CoI), van der Vegt, W. (CoI), Berkhout, J. (CoI), Nyamsuren, E. (CoI), Kluijfhout, E. (CoI) & Nadolski, R. (CoI)
European Union
1/02/15 → 31/07/19
Project: Research

Cite this

Sirbu, M.-D., Dascalu, M., Crossley, S. A., McNamara, D. S., Barnes, T., Lynch, C. F., & Trausan-Matu, S. (2018). Exploring Online Course Sociograms Using Cohesion Network Analysis. In R. Luckin, K. Porayska-Pomsta, B. du Boulay, M. Mavrikis, C. Penstein Rosé, B. McLaren, R. Martinez-Maldonado, & H. U. Hoppe (Eds.), Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II (Vol. Part II, pp. 337–342). Springer. https://doi.org/10.1007/978-3-319-93846-2_63

Sirbu, Maria-Dorinela ; Dascalu, Mihai ; Crossley, Scott A. et al. / Exploring Online Course Sociograms Using Cohesion Network Analysis. Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. editor / Rose Luckin ; Kaska Porayska-Pomsta ; Benedict du Boulay ; Manolis Mavrikis ; Carolyn Penstein Rosé ; Bruce McLaren ; Roberto Martinez-Maldonado ; H. Ulrich Hoppe. Vol. Part II Cham : Springer, 2018. pp. 337–342 (Lecture Notes in Computer Science, Vol. 10948).

@inproceedings{c3ce468de57b432cbcad85fe27236e8a,

title = "Exploring Online Course Sociograms Using Cohesion Network Analysis",

abstract = "Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in online courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students{\textquoteright} behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and toidentify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.",

keywords = "Cohesion Network Analysis, Interaction patterns, Online courses, Participants clustering, Sociograms",

author = "Maria-Dorinela Sirbu and Mihai Dascalu and Crossley, {Scott A.} and McNamara, {Danielle S.} and T. Barnes and C.F. Lynch and Stefan Trausan-Matu",

note = "This publication reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains.; International Conference, AIED 2018, AIED 2018 ; Conference date: 27-06-2018 Through 30-06-2018",

year = "2018",

doi = "10.1007/978-3-319-93846-2_63",

language = "English",

isbn = "9783319938455",

volume = "Part II",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "337–342",

editor = "Rose Luckin and Kaska Porayska-Pomsta and {du Boulay}, Benedict and Manolis Mavrikis and {Penstein Ros{\'e}}, Carolyn and Bruce McLaren and Roberto Martinez-Maldonado and Hoppe, {H. Ulrich}",

booktitle = "Artificial Intelligence in Education",

url = "https://aied2018.utscic.edu.au/",

}

Sirbu, M-D, Dascalu, M, Crossley, SA, McNamara, DS, Barnes, T, Lynch, CF & Trausan-Matu, S 2018, Exploring Online Course Sociograms Using Cohesion Network Analysis. in R Luckin, K Porayska-Pomsta, B du Boulay, M Mavrikis, C Penstein Rosé, B McLaren, R Martinez-Maldonado & HU Hoppe (eds), Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. vol. Part II, Springer, Cham, Lecture Notes in Computer Science, vol. 10948, pp. 337–342, International Conference, AIED 2018, London, United Kingdom, 27/06/18. https://doi.org/10.1007/978-3-319-93846-2_63

Exploring Online Course Sociograms Using Cohesion Network Analysis. / Sirbu, Maria-Dorinela; Dascalu, Mihai; Crossley, Scott A. et al.
Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. ed. / Rose Luckin; Kaska Porayska-Pomsta; Benedict du Boulay; Manolis Mavrikis; Carolyn Penstein Rosé; Bruce McLaren; Roberto Martinez-Maldonado; H. Ulrich Hoppe. Vol. Part II Cham: Springer, 2018. p. 337–342 (Lecture Notes in Computer Science, Vol. 10948).

Research output: Chapter in Book/Report/Conference proceeding › Conference Article in proceeding › Academic › peer-review

TY - GEN

T1 - Exploring Online Course Sociograms Using Cohesion Network Analysis

AU - Sirbu, Maria-Dorinela

AU - Dascalu, Mihai

AU - Crossley, Scott A.

AU - McNamara, Danielle S.

AU - Barnes, T.

AU - Lynch, C.F.

AU - Trausan-Matu, Stefan

N1 - This publication reflects only the author's view and the European Commission is not responsible for any use that may be made of the information it contains.

PY - 2018

Y1 - 2018

N2 - Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in online courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students’ behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and toidentify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.

AB - Massive Open Online Courses (MOOCs) have become an important platform for teaching and learning because of their ability to deliver educational accessibility across time and distance. Online learning environments have also provided new research opportunities to examine learning success at a large scale. One data tool that has been proven effective in exploring student success in online courses has been Cohesion Network Analysis (CNA), which offers the ability to analyze discourse structure in collaborative learning environments and facilitate the identification of learner interaction patterns. These patterns can be used to predict students’ behaviors such as dropout rates and performance. The focus of the current paper is to identify sociograms (i.e., interaction graphs among participants) generated through CNA on course forum discussions and toidentify temporal trends among students. Here, we introduce extended CNA visualizations available in the ReaderBench framework. These visualizations can be used to convey information about interactions between participants in online forums, as well as corresponding student clusters within specific timeframes.

KW - Cohesion Network Analysis

KW - Interaction patterns

KW - Online courses

KW - Participants clustering

KW - Sociograms

U2 - 10.1007/978-3-319-93846-2_63

DO - 10.1007/978-3-319-93846-2_63

M3 - Conference Article in proceeding

SN - 9783319938455

VL - Part II

T3 - Lecture Notes in Computer Science

SP - 337

EP - 342

BT - Artificial Intelligence in Education

A2 - Luckin, Rose

A2 - Porayska-Pomsta, Kaska

A2 - du Boulay, Benedict

A2 - Mavrikis, Manolis

A2 - Penstein Rosé, Carolyn

A2 - McLaren, Bruce

A2 - Martinez-Maldonado, Roberto

A2 - Hoppe, H. Ulrich

PB - Springer

CY - Cham

T2 - International Conference, AIED 2018

Y2 - 27 June 2018 through 30 June 2018

ER -

Sirbu MD, Dascalu M, Crossley SA, McNamara DS, Barnes T, Lynch CF et al. Exploring Online Course Sociograms Using Cohesion Network Analysis. In Luckin R, Porayska-Pomsta K, du Boulay B, Mavrikis M, Penstein Rosé C, McLaren B, Martinez-Maldonado R, Hoppe HU, editors, Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30, 2018, Proceedings, Part II. Vol. Part II. Cham: Springer. 2018. p. 337–342. (Lecture Notes in Computer Science, Vol. 10948). doi: 10.1007/978-3-319-93846-2_63

Exploring Online Course Sociograms Using Cohesion Network Analysis

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Projects

Rage: Realising an Applied Gaming Eco-system

Cite this