DocumentCode
259534
Title
Learning Analytics in Linked Open Online Courses
Author
Hover, Kai Michael ; Muhlhauser, Max
Author_Institution
Dept. of Comput. Sci., Tech. Univ. Darmstadt, Darmstadt, Germany
fYear
2014
fDate
10-12 Dec. 2014
Firstpage
369
Lastpage
374
Abstract
Learning offerings and learners´ activities are increasingly moving to decentralized learning environments in the cloud. The advent of MOOC platforms gives a good example for this ongoing evolution. However, most learning applications use proprietary data models. As learners nowadays not only use only one e-learning offering, the analysis of learner activities is challenging in such decentralized and heterogeneous learning environments. To address this problem, we present three different learning applications and show the potential of exploiting Semantic Web technologies.
Keywords
computer aided instruction; educational courses; semantic Web; MOOC platforms; decentralized learning environments; e-learning offering; learner activities; learning analytics; learning applications; linked open online courses; proprietary data models; semantic Web technologies; Data models; Discussion forums; Distributed databases; Electronic learning; Interoperability; Ontologies; Semantic Web; Interoperability; Learning Analytics; Lecture Recording; Ontology; Semantic Web;
fLanguage
English
Publisher
ieee
Conference_Titel
Multimedia (ISM), 2014 IEEE International Symposium on
Conference_Location
Taichung
Print_ISBN
978-1-4799-4312-8
Type
conf
DOI
10.1109/ISM.2014.51
Filename
7033053
Link To Document