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
AU - Barnes, Tiffany
AU - Lynch, Collin F.
AU - Trausan-Matu, Stefan
N1 - Funding Information:
Acknowledgments. This research was partially supported by the 644187 EC H2020 RAGE project and the FP7 2008-212578 LTfLL project. In addition, this research was supported in part by the National Science Foundation (DRL-1418378). Ideas expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Funding Information:
This research was partially supported by the 644187 EC H2020 RAGE project and the FP7 2008-212578 LTfLL project. In addition, this research was supported in part by the National Science Foundation (DRL-1418378). Ideas expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
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 on-line 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.
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 on-line 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.
KW - Cohesion network analysis
KW - Interaction patterns
KW - Online courses
KW - Participants clustering
KW - Sociograms
UR - http://www.scopus.com/inward/record.url?scp=85049359367&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049359367&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93846-2_63
DO - 10.1007/978-3-319-93846-2_63
M3 - Conference contribution
AN - SCOPUS:85049359367
SN - 9783319938455
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 337
EP - 342
BT - Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
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 Verlag
T2 - 19th International Conference on Artificial Intelligence in Education, AIED 2018
Y2 - 27 June 2018 through 30 June 2018
ER -