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Time- and learner-dependent Gaussian hidden Markov model for writing-process sequence analysis

Research Method · All research methods
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

The article proposes an extension of the Gaussian hidden Markov model that "incorporates parameters representing temporal change in a subtask appearance distribution for each learner," so that writing-process sequence patterns can be estimated from keystroke log data. The model treats the writing process as a sequence of subtasks such as planning, formulation, and revision, and is intended to relieve instructors of analyzing sequence patterns for all learners manually.

Accounts

  • Time- and learner-dependent Gaussian hidden Markov model for estimating writing-process sequence patterns from keystroke logs: The article proposes an extension of the Gaussian hidden Markov model that "incorporates parameters representing temporal change in a subtask appearance distribution for each learner," so that writing-process sequence patterns can be estimated from keystroke log data. The model treats the writing process as a sequence of subtasks such as planning, formulation, and revision, and is intended to relieve instructors of analyzing sequence patterns for all learners manually. (Uto et al. (2020))

Claims

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Key Sources

  • Uto, M., Miyazawa, Y., Kato, Y., Nakajima, K., & Kuwata, H. (2020). Time- and Learner-Dependent Hidden Markov Model for Writing Process Analysis Using Keystroke Log Data. Int J Artif Intell Educ, 30, 271–298. https://doi.org/10.1007/s40593-019-00189-9
Page metadata
Field Value
type research-method
id time-and-learner-dependent-gaussian-hidden-markov-model-for-writing-process-sequence-analy
title Time- and learner-dependent Gaussian hidden Markov model for writing-process sequence analysis
description The article proposes an extension of the Gaussian hidden Markov model that "incorporates parameters representing temporal change in a subtask appearance distribution for each learner," so that writing-process sequence patterns can be estimated from keystroke log data.
status review
generated by process:settle-candidates on 2026-10-28
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