Learning to Recognize Time Series: Combining ARMA models with memory-based learning
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Modeling of multivariate time series using hidden markov models
Modeling of multivariate time series using hidden markov models
A new direction for log file analysis in CSCL: experiences with a spatio-temporal metric
CSCL '05 Proceedings of th 2005 conference on Computer support for collaborative learning: learning 2005: the next 10 years!
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Clustering of time series data-a survey
Pattern Recognition
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Analysis of collaborative activities is a popular research area in CSCW and CSCL fields since it provides useful information for improving the quality and efficiency of collaborative activities. Prior research has focused on qualitative methods for evaluating collaboration while machine learning algorithms and logfile analysis have been proposed for post-assessment. In this paper we propose the use of time series analysis techniques in order to classify synchronous, collaborative learning activities. Time is an important aspect of collaboration, especially when it takes place synchronously, and can reveal the underlying group dynamics. Therefore time series analysis should be considered as an option when we wish to have a clear view of the process and final outcome of a collaborative activity. We argue that classification of collaborative activities based on time series will also reflect on their qualitative aspects. Collaborative sessions that share similar time series, will also share similar qualitative properties.