Time- and Learner-Dependent Hidden Markov Model for Writing Process Analysis Using Keystroke Log Data

Autor/inn/en	Uto, Masaki; Miyazawa, Yoshimitsu; Kato, Yoshihiro; Nakajima, Koji; Kuwata, Hajime
Titel	Time- and Learner-Dependent Hidden Markov Model for Writing Process Analysis Using Keystroke Log Data
Quelle	In: International Journal of Artificial Intelligence in Education, 30 (2020) 2, S.271-298 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Uto, Masaki)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1560-4292
DOI	10.1007/s40593-019-00189-9
Schlagwörter	Markov Processes; Writing Processes; Writing Skills; Keyboarding (Data Entry); Computation; Evaluation Methods; Simulation + Suchen Sie Ihr Suchwort? Markowscher Prozess; Writing skill; Schreibfertigkeit; Texterfassung; Simulation program; Simulationsprogramm
Abstract	Teaching writing strategies based on writing processes has attracted wide attention as a method for developing writing skills. The writing process can be generally defined as a sequence of subtasks, such as planning, formulation, and revision. Therefore, instructor feedback is often given based on sequence patterns of those subtasks. For such feedback, instructors need to analyze sequence patterns for all learners, which becomes problematic as the number of learners increases. To resolve this problem, this study proposes a new machine-learning method that estimates sequence patterns from keystroke log data. Specifically, we propose an extension of the Gaussian hidden Markov model that incorporates parameters representing temporal change in a subtask appearance distribution for each learner. Furthermore, we propose a collapsed Gibbs sampling algorithm as the parameter estimation method for the proposed model. We demonstrate effectiveness of the proposed model by applying it to actual keystroke log datasets. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01