Modeling MOOC Student Behavior with Two-Layer Hidden Markov Models

Autor/inn/en	Geigle, Chase; Zhai, ChengXiang
Titel	Modeling MOOC Student Behavior with Two-Layer Hidden Markov Models
Quelle	In: Journal of Educational Data Mining, 9 (2017) 1, S.1-24 (24 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2157-2100
Schlagwörter	Large Group Instruction; Online Courses; Educational Technology; Technology Uses in Education; Student Behavior; Markov Processes; Models; Probability; Humanities Instruction; Computer Science Education + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Student behaviour; Schülerverhalten; Markowscher Prozess; Analogiemodell; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Geisteswissenschaftlicher Unterricht; Computer science lessons; Informatikunterricht
Abstract	Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. To address this problem, we propose a student behavior representation method alongside a method for automatically discovering those student behavior patterns by leveraging the click log data that can be obtained from the MOOC platform itself. Specifically, we propose the use of a two-layer hidden Markov model (2L-HMM) to extract our desired behavior representation, and show that patterns extracted by such a 2L-HMM are interpretable and meaningful. We demonstrate that the proposed 2L-HMM can also be used to extract latent features from student behavioral data that correlate with educational outcomes. (As Provided).
Anmerkungen	International Working Group on Educational Data Mining. e-mail: jedm.editor@gmail.com; Web site: http://www.educationaldatamining.org/JEDM/index.php/JEDM/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01