Abstract
In this chapter, we outline the uses of point processes and related methods for modeling temporal dependence in human interactions. We begin by describing our example, which was drawn from teamwork in sports. We then discuss three interrelated steps in analyzing the data: (a) the problem of defining and detecting temporal dependence among the activities of team members, (b) characterization of the dependence in terms of temporal clustering, and (c) the use of the Hawkes process to model the clustering. The third step provides a parametric model for describing and comparing statistical regularities of the interactions among individual team members or subsets of team members. We conclude by considering how this approach can capture aspects of team interaction that might be relevant for developing performance-based assessments involving collaborative problem solving.
This work was conducted while Alina A. von Davier was employed with Educational Testing Service.
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Notes
- 1.
For example, http://www.basketballgeek.com/data/.
References
Barabási, A. L. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435, 207–211. doi:10.1038/nature03526.1
Brillinger, D. R. (1975). The identification of point process systems. Annals of Probability, 3, 909–929.
Brillinger, D. R. (2004). Some data analyses using mutual information. Brazilian Journal of Probability and Statistics, 18, 163–182.
Budescu, D. V. (1985). Analysis of dichotomous variables in the presence of serial dependence. Psychological Bulletin, 97, 547–561.
Cover, T. M., & Thomas, J. A. (2005). Elements of information theory. New York, NY, USA: Wiley.
Crane, R., & Sornette, D. (2008). Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences of the United States of America, 105, 15649–15653.
Daley, D. J., & Vera-Jones, D. (2003). An introduction to the theory of point processes: Elementary theory and methods (2nd ed., Vol. 1). New York, NY, USA: Springer.
Griffin, P., & Care, E. (2015). Assessment and teaching of 21st century skills: Methods and approach. New York, NY, USA: Springer.
Griffin, P., McGaw, B., & Care, E. (2012). Assessment and teaching of 21st century skills. New York, NY, USA: Springer.
Halpin, P. F., & De Boeck, P. (2013). Modelling dyadic interaction with Hawkes processes. Psychometrika, 78, 793–814. doi:10.1007/s11336-013-9329-1
Hao, J., Liu, L., von Davier, A., & Kyllonen, P. (2015). Assessing collaborative problem solving with simulation-based tasks. In O. Lindwall, P. Häkkine, T. Koschmann, P. Tchounikine, & S. Ludvigsen (Eds.), Exploring the material conditions of learning: The computer supported collaborative learning conference (Vol. 2, pp. 544–547). Gothenberg, Sweden: International Society of the Learning Sciences.
Halpin, P. F. (2013). A scalable EM algorithm for Hawkes processes. In R. E. Millsap, L. A. van der Ark, D. M. Bolt, & C. M. Woods (Eds.), New developments in quantitative psychology: Proceedings of the 77th international meeting of the psychometric society (pp. 403–414). New York: Springer.
Hawkes, A. G. (1971). Point spectra of some mutually exciting point processes Point spectra of some mutually exciting point processes. Journal of the Royal Statistical Society, Series B33, 104, 438–443. doi:10.1073/pnas.0703993104
Hawkes, A. G., & Oakes, D. (1974). A cluster process representation of a self-exciting process. Journal of Applied Probability, 11, 493–503.
Liu, L., Hao, J., von Davier, A., Kyllonen, P., & Zapata-Rivera, D. (2015). A tough nut to crack: Measuring collaborative problem solving. In Y. Rosen, S. Ferrara, & M. Mosharraf (Eds.), Handbook of research on computational tools for real-world skill development (pp. 344–359). Hershey, PA, USA: IGI-Global.
Matsubara, Y., Sakurai, Y., Prakash, B. A., Li, L., & Faloutsos, C. (2012). Rise and fall patterns of information diffusion: Model and implications. In KDD’12: Proceedings of the 18th ACM SIGKDD (pp. 6–14). New York, NY, USA: ACM.
Oliveira, J. G., & Vazquez, A. (2009). Impact of interactions on human dynamics Impact of interactions on human dynamics. Physica A, 388, 187–192.
Paninski, L. (2003). Estimation of entropy and mutual information. Neural Computation, 15, 1191–1253. doi:10.1162/089976603321780272
Rasmussen, J. G. (2012). Bayesian inference for Hawkes processes. Methodology and Computing in Applied Probability, 15, 623–642. doi:10.1007/s11009-011-9272-5
von Davier, A. A., & Halpin, P. F. (2013). Collaborative problem solving and the assessment of cognitive skills: Psychometric considerations (ETS Research Report No. RR-13-41). Princeton, NJ, USA: ETS.
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Halpin, P.F., von Davier, A.A. (2017). Modeling Collaboration Using Point Processes. In: von Davier, A., Zhu, M., Kyllonen, P. (eds) Innovative Assessment of Collaboration. Methodology of Educational Measurement and Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-33261-1_15
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