Discovery and Temporal Analysis of MOOC Study Patterns

Autor/inn/en	Boroujeni, Mina Shirvani; Dillenbourg, Pierre
Titel	Discovery and Temporal Analysis of MOOC Study Patterns
Quelle	In: Journal of Learning Analytics, 6 (2019) 1, S.16-33 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Online Courses; Large Group Instruction; Learning Processes; Study Habits; Research Methodology; Data Collection; Markov Processes; Learning Strategies; Video Technology; Assignments; Learning Analytics + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Learning process; Lernprozess; Study behavior; Study behaviour; Studienverhalten; Research method; Forschungsmethode; Data capture; Datensammlung; Markowscher Prozess; Learning methode; Learning techniques; Lernmethode; Lernstrategie; Assignment; Auftrag; Zuweisung
Abstract	The large-scale and granular interaction data collected in online learning platforms such as massive open online courses (MOOCs) provide unique opportunities to better understand individuals' learning processes and could facilitate the design of personalized and more effective support mechanisms for learners. In this paper, we present two different methods of extracting study patterns from activity sequences. Unlike most of the previous works, with post hoc analysis of activity patterns, our proposed methods could be deployed during the course and enable the learners to receive real-time support and feedback. In the first method, following a hypothesis-driven approach, we extract predefined patterns from learners' interactions with the course materials. We then identify and analyze different longitudinal profiles among learners by clustering their study pattern sequences during the course. Our second method is a data-driven approach to discover latent study patterns and track them over time in a completely unsupervised manner. We propose a clustering pipeline to model and cluster activity sequences at each time step and then search for matching clusters in previous steps to enable tracking over time. The proposed pipeline is general and allows for analysis at different levels of action granularity and time resolution in various online learning environments. Experiments with synthetic data show that our proposed method can accurately detect latent study patterns and track changes in learning behaviours. We demonstrate the application of both methods on a MOOC dataset and study the temporal dynamics of learners' behaviour in this context. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01