Analysis of Dynamic Resource Access Patterns in Online Courses

Autor/inn/en	Hecking, Tobias; Ziebarth, Sabrina; Hoppe, H. Ulrich
Titel	Analysis of Dynamic Resource Access Patterns in Online Courses
Quelle	In: Journal of Learning Analytics, 1 (2014) 3, S.34-60 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1929-7750
Schlagwörter	Online Courses; Blended Learning; Educational Technology; Technology Uses in Education; Large Group Instruction; Masters Programs; Graduate Students; Learning Activities; Use Studies; Social Networks; Network Analysis; Data Collection; Data Analysis; Educational Resources; Foreign Countries; Germany + Suchen Sie Ihr Suchwort? Online course; Online-Kurs; Unterrichtsmedien; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Magister course; Magisterstudiengang; Graduate Study; Student; Students; Aufbaustudium; Graduiertenstudium; Hauptstudium; Studentin; Lernaktivität; Benutzerschulung; Social network; Soziales Netzwerk; Netzplantechnik; Data capture; Datensammlung; Auswertung; Bildungsmittel; Ausland; Deutschland
Abstract	This paper presents an analysis of resource access patterns in two recently conducted online courses. One of these has been a master level university lecture taught as a blended learning course with a wide range of online learning activities and materials, including collaborative wikis, self-tests, and thematic videos. The other course has been offered in the form of a MOOC. As a specialty of this course, master level students from two different universities could participate as a regular university class and receive credits for successful completion. In both courses, online learning resources such as videos, scientific literature, and wikis played a central role. In this context, the motivation for our research was to investigate characteristic patterns of resource usage of the learners. In order to gain deeper insights into the usage of learning materials, we have adapted methods from social network analysis and applied them to dynamic bipartite student-resource networks built from event logs of the students' resource access. In particular, we describe the clustering of students and resources in such networks and propose a method to identify patterns of the cluster evolution over time. (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