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Clustering and Sequential Pattern Mining of Online Collaborative Learning Data
June 2009 (vol. 21 no. 6)
pp. 759-772
ASCII Text
x 
 
Dilhan Perera, Judy Kay, Irena Koprinska, Kalina Yacef, Osmar R. Zaïane, "Clustering and Sequential Pattern Mining of Online Collaborative Learning Data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 6, pp. 759-772, June, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.138,
author = {Dilhan Perera and Judy Kay and Irena Koprinska and Kalina Yacef and Osmar R. Zaïane},
title = {Clustering and Sequential Pattern Mining of Online Collaborative Learning Data},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {21},
number = {6},
issn = {1041-4347},
year = {2009},
pages = {759-772},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.138},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Clustering and Sequential Pattern Mining of Online Collaborative Learning Data
IS - 6
SN - 1041-4347
SP759
EP772
EPD - 759-772
A1 - Dilhan Perera,
A1 - Judy Kay,
A1 - Irena Koprinska,
A1 - Kalina Yacef,
A1 - Osmar R. Zaïane,
PY - 2009
KW - Data mining
KW - clustering
KW - sequential pattern mining
KW - learning group work skills
KW - collaborative learning
KW - computer-assisted instruction.
VL - 21
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Dilhan Perera, University of Sydney, Sydney
Judy Kay, University of Sydney, Sydney
Irena Koprinska, University of Sydney, Sydney
Kalina Yacef, University of Sydney, Sydney
Osmar R. Zaïane, University of Alberta, Edmonton, Alberta
Group work is widespread in education. The growing use of online tools supporting group work generates huge amounts of data. We aim to exploit this data to support mirroring: presenting useful high-level views of information about the group, together with desired patterns characterizing the behavior of strong groups. The goal is to enable the groups and their facilitators to see relevant aspects of the group's operation and provide feedback if these are more likely to be associated with positive or negative outcomes and indicate where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. The context is a senior software development project where students use the collaboration tool TRAC. We extract patterns distinguishing the better from the weaker groups and get insights in the success factors. The results point to the importance of leadership and group interaction, and give promising indications if they are occurring. Patterns indicating good individual practices were also identified. We found that some key measures can be mined from early data. The results are promising for advising groups at the start and early identification of effective and poor practices, in time for remediation.

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Index Terms:
Data mining, clustering, sequential pattern mining, learning group work skills, collaborative learning, computer-assisted instruction.
Citation:
Dilhan Perera, Judy Kay, Irena Koprinska, Kalina Yacef, Osmar R. Zaïane, "Clustering and Sequential Pattern Mining of Online Collaborative Learning Data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 6, pp. 759-772, June 2009, doi:10.1109/TKDE.2008.138
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