Empirical Analysis of Errors on Human-Generated Learning Objects Metadata

被引:0
|
作者
Cechinel, Cristian [1 ]
Sanchez-Alonso, Salvador [2 ]
Angel Sicilia, Miguel [2 ]
机构
[1] Fed Univ Pampa, Comp Engn Course, BR-96400970 Bage, RS, Brazil
[2] Univ Alcala de Henares, Dept Comp Sci, E-28871 Alcala De Henares, Spain
来源
METADATA AND SEMANTIC RESEARCH, PROCEEDINGS | 2009年 / 46卷
关键词
Metadata errors; IEEE LOM; learning objects;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Learning object metadata is considered crucial for the right management of learning objects stored in public repositories. Search operations, in particular, rely on the quality of these metadata as an essential precondition for finding results adequate to users requirements and needs. However, learning object metadata are not always reliable, as many factors have a negative influence in metadata quality (human annotators not having the minimum skills, unvoluntary mistakes, lack of information, for instance). This paper analyses human-generated learning object metadata records described according to the IEEE LOM standard, identifies the most significant errors committed and points out which parts of the standard should be improved for the sake of quality.
引用
收藏
页码:60 / 70
页数:11
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