Time Series Analysis of Collaborative Activities
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.
Keywordstime-series collaboration classification logfile analysis
Unable to display preview. Download preview PDF.
- 1.Barros, B., Verdejo, M.F.: Analysing student interaction processes in order to improve collaboration. The DEGREE approach. International Journal of Artificial Intelligence in Education 11(3), 221–241 (2000)Google Scholar
- 2.Schümmer, T., Strijbos, J.-W., Berkel, T.: A new direction for log file analysis in CSCL: Experiences with a spatio-temporal metric. In: Koschmann, T., Suthers, D., Chan, T.W. (eds.) Computer Supported Collaborative Learning 2005: The next 10 years!, pp. 567–576 (2005)Google Scholar
- 3.Kahrimanis, G., Meier, A., Chounta, I.-A., Voyiatzaki, E., Spada, H., Rummel, N., Avouris, N.: Assessing Collaboration Quality in Synchronous CSCL Problem-Solving Activities: Adaptation and Empirical Evaluation of a Rating Scheme. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 267–272. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 6.Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time series analysis, Forecasting and Control, 4th edn. Wiley (2008)Google Scholar
- 7.Chatfield, C.: The Analysis of Time Series: An Introduction. Chapman & Hall (2003)Google Scholar
- 8.Avouris, N., Margaritis, M., Komis, V.: Modelling interaction during small-group synchronous problem-solving activities: The Synergo approach. In: 2nd Int. Workshop on Designing Computational Models of Collaborative Learning Interaction, ITS 2004, Maceio (2004)Google Scholar
- 9.Kahrimanis, G., Chounta, I.-A., Avouris, N.: Study of correlations between logfile-based metrics of interaction and the quality of synchronous collaboration. International Reports on Socio-Informatics (IRSI) 7(1), 24–31 (2010)Google Scholar
- 11.Deng, K., Moore, A., Nechyba, M.: Learning to recognize time series: Combining arma models with memory-based learning. In: IEEE CIRA, pp. 246–250 (1997)Google Scholar
- 13.Chao, S., Wong, F., Lam, H.-L., Vai, M.-I.: Blind biosignal classification framework based on DTW algorithm. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 4, pp. 1684–1689 (2011)Google Scholar
- 14.Giorgino, T.: Computing and visualizing dynamic time warping alignments in R: the dtw package. Journal of Statistical Software 31(7), 1–24 (2009)Google Scholar
- 15.Kirshner, S.: Modeling of multivariate time series using hidden Markov models. PhD thesis, University of California (2005)Google Scholar