A Modern Approach to Transition Analysis and Process Mining with Markov Models in Education
: Helske, Jouni; Helske, Satu; Saqr, Mohammed; López-Pernas, Sonsoles; Murphy, Keefe
: Saqr, M., López-Pernas, S.
Publisher: Springer Nature Switzerland
: 2024
: Learning Analytics Methods and Tutorials
: 381
: 427
: 978-3-031-54463-7
: 978-3-031-54464-4
DOI: https://doi.org/10.1007/978-3-031-54464-4_12
: https://doi.org/10.1007/978-3-031-54464-4_12
: https://research.utu.fi/converis/portal/detail/Publication/458369211
This chapter presents an introduction to Markovian modelling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapters, Markovian models are probabilistic models, focusing on the transitions between states instead of studying sequences as a whole. The chapter provides an introduction to this method and differentiates between its most common variations: first-order Markov models, hidden Markov models, mixture Markov models, and mixture hidden Markov models. In addition to a thorough explanation and contextualisation within the existing literature, the chapter provides a step-by-step tutorial on how to implement each type of Markovian model using the R package seqHMM. The chapter also provides a complete guide to performing stochastic process mining with Markovian models as well as plotting, comparing and clustering different process models.
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JH and SH were supported by Research Council of Finland (PREDLIFE: Towards well-informed decisions: Predicting long-term effects of policy reforms on life trajectories, decision numbers 331817 and 331816, and Research Flagship INVEST: Inequalities, Interventions and New Welfare State, decision number 345546). SH was also supported by the Strategic Research Council (SRC), FLUX consortium: Family Formation in Flux – Causes, Consequences, and Possible Futures (decision numbers: 345130 and 345130). MS was supported by Research Council of Finland (TOPEILA: Towards precision education: Idiographic learning analytics, decision number 350560).
