Empirical Analysis of Errors on Human-Generated Learning Objects Metadata

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.