A Modern Approach to Transition Analysis and Process Mining with Markov Models in Education

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dc.contributor.authorHelske, Jouni
dc.contributor.authorHelske, Satu
dc.contributor.authorSaqr, Mohammed
dc.contributor.authorLópez-Pernas, Sonsoles
dc.contributor.authorMurphy, Keefe
dc.contributor.organizationfi=INVEST tutkimuskeskus ja lippulaiva|en=INVEST Research Flagship Centre|
dc.contributor.organization-code1.2.246.10.2458963.20.11531668876
dc.converis.publication-id458369211
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/458369211
dc.date.accessioned2025-08-27T22:06:10Z
dc.date.available2025-08-27T22:06:10Z
dc.description.abstract<p>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.<br></p>
dc.format.pagerange381
dc.format.pagerange427
dc.identifier.eisbn978-3-031-54464-4
dc.identifier.isbn978-3-031-54463-7
dc.identifier.olddbid201641
dc.identifier.oldhandle10024/184668
dc.identifier.urihttps://www.utupub.fi/handle/11111/48675
dc.identifier.urlhttps://doi.org/10.1007/978-3-031-54464-4_12
dc.identifier.urnURN:NBN:fi-fe2025082785455
dc.okm.affiliatedauthorHelske, Jouni
dc.okm.affiliatedauthorHelske, Satu
dc.okm.discipline112 Statistics and probabilityen_GB
dc.okm.discipline113 Computer and information sciencesen_GB
dc.okm.discipline516 Educational sciencesen_GB
dc.okm.discipline112 Tilastotiedefi_FI
dc.okm.discipline113 Tietojenkäsittely ja informaatiotieteetfi_FI
dc.okm.discipline516 Kasvatustieteetfi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA3 Book
dc.publisherSpringer
dc.publisher.countrySwitzerlanden_GB
dc.publisher.countrySveitsifi_FI
dc.publisher.country-codeCH
dc.publisher.isbn978-81-322;978-3-540;978-3-642;978-3-662;978-3-7908;978-3-8274;978-3-8347;978-90-481;978-94-007;978-94-009;978-94-010;978-94-011;978-94-015;978-94-017;978-94-024;978-0-387;978-0-8176;978-1-4419;978-1-4612;978-1-4613;978-1-4614;978-1-4615;978-1-4684;978-1-4757;978-1-4899;978-1-4939;978-1-5041;978-3-319;978-1-4020;978-0-85729;978-1-4471;978-1-84628;978-1-84800;978-1-84882;978-1-84996;978-1-85233;978-3-211;978-3-7091;978-4-431;978-3-322;978-3-409;978-3-531;978-3-658;978-3-663;978-3-8100;978-981-287;978-981-10;978-981-13;978-3-030;978-981-32;978-981-15;978-981-16;978-981-329;978-981-334;978-981-336;978-3-031;978-981-19;
dc.relation.doi10.1007/978-3-031-54464-4_12
dc.source.identifierhttps://www.utupub.fi/handle/10024/184668
dc.titleA Modern Approach to Transition Analysis and Process Mining with Markov Models in Education
dc.title.bookLearning Analytics Methods and Tutorials
dc.year.issued2024

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