A3 Refereed book chapter or chapter in a compilation book
A Modern Approach to Transition Analysis and Process Mining with Markov Models in Education
Authors: Helske, Jouni; Helske, Satu; Saqr, Mohammed; López-Pernas, Sonsoles; Murphy, Keefe
Editors: Saqr, M., López-Pernas, S.
Publisher: Springer Nature Switzerland
Publication year: 2024
Book title : Learning Analytics Methods and Tutorials
First page : 381
Last page: 427
ISBN: 978-3-031-54463-7
eISBN: 978-3-031-54464-4
DOI: https://doi.org/10.1007/978-3-031-54464-4_12
Web address : https://doi.org/10.1007/978-3-031-54464-4_12
Self-archived copy’s web address: 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.
Downloadable publication This is an electronic reprint of the original article. |  |
Funding information in the publication:
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).
