Paper Info
Title
Studying Social Tagging Systems in Learning Object Repositories: An Empirical Study of the Tag Vocabulary Growth
Abstract
In the field of Technology-enhanced Learning (TeL), social tagging has been applied to Learning Object Repositories (LORs) mainly as a means to offer an alternative way of classifying the LOs based on the tag vocabulary (i.e. the collection of tags created by the end-users of the LOs). Nevertheless, in order to be able to understand how a social tagging system performs and whether it can deliver the aforementioned goal, it is important to be able to investigate the behaviour of the tag vocabulary, which constitutes the core component of a social tagging system. Within this context, many studies have been conducted regarding the growth of the tag vocabularies of social tagging systems but there are only sporadic studies for investigating this issue in the field of LORs. This paper aims to contribute in studying how social tagging systems perform in the context of LORs by investigating tag vocabulary growth in OpenScienceResources Repository, a Science Education domain-specific repository with a rich dataset operating in Europe for 5 years.
Year
DOI
Venue
2016
10.1109/ICALT.2016.118
2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)
Keywords
Field
DocType
social tagging,social tagging system,learning object repository,tag vocabulary,tag growth,tag entropy
World Wide Web,Information retrieval,Computer science,Learning object,Vocabulary,Empirical research,Tag system,Science education
Conference
ISSN
ISBN
Citations 
2161-3761
978-1-4673-9042-2
0
PageRank 
References 
Authors
0.34
9
2
Name
Order
Citations
PageRank
Panagiotis Zervas19919.96
Demetrios G. Sampson21310247.68