Statistical and stochastic analysis of sequence data

Ming Ming CHIU, Peter REIMANN

Research output: Chapter in Book/Report/Conference proceedingChapters


Two common CSCL questions regarding analyses of temporal data, such as event sequences, are: (i) What variables are related to event attributes? and (ii) what is the process (or what are the processes) that generated the events? The first question is best answered with statistical methods, the second with stochastic or deterministic process modeling methods. This chapter provides an overview of statistical and stochastic methods of direct relevance to CSCL research. Many of the statistical analyses are integrated into statistical discourse analysis. From the stochastic modeling repertoire, the basic hidden Markov model as well as recent extensions is introduced, ending with dynamic Bayesian models as the current best integration. Looking into the near future, we identify opportunities for a closer alignment of qualitative with quantitative methods for temporal analysis, afforded by developments such as automization of quantitative methods and advances in computational modeling. Copyright © 2021 Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationInternational handbook of computer-supported collaborative learning
EditorsUlrike CRESS, Carolyn ROSÉ, Alyssa Friend WISE, Jun OSHIMA
Place of PublicationCham
ISBN (Electronic)9783030652913
ISBN (Print)9783030652937, 9783030652906
Publication statusPublished - 2021


Chiu, M. M., & Reimann, P. (2021). Statistical and stochastic analysis of sequence data. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning (pp. 533-550). Cham: Springer.


  • Statistical discourse analysis
  • Time analysis
  • Stochastic models
  • Process mining


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