Linked Data as facilitator for TEL recommender systemsin research & practice

2nd Workshop on Recommender Systems in Technology Enhanced Learning 2012

Stefan Dietze1

Others  :  http://ceur-ws.org/Vol-896/keynote.pdf
PID  :  43359

来源: CEUR

【 摘 要 】

Personalisation, adaptation and recommendation are central features of TEL environments. In this context, information retrieval techniques are ap- plied as part of TEL recommender systems to filter and deliver learning re- sources according to user preferences and requirements. However, the suitabil- ity and scope of possible recommendations is fundamentally dependent on thequality and quantity of available data, for instance, metadata about TEL re- sources as well as users. On the other hand, throughout the last years, theLinked Data (LD) movement has succeeded to provide a vast body of well- interlinked and publicly accessible Web data. This in particular includes LinkedData of explicit or implicit educational nature. The potential of LD to facilitateTEL recommender systems research and practice is discussed in this paper. Inparticular, an overview of most relevant LD sources and techniques is provided,together with a discussion of their potential for the TEL domain in general andTEL recommender systems in particular based on insights from highly relatedEuropean projects, mEducator and LinkedUp.