Tags and self-organisation: a metadata ecology for learning resources in a multilingual context

This thesis studies social tagging of learning resources in a multilingual context. Social tagging and its end products, tags, are regarded as part of the learning resources metadata ecology. The term “metadata ecology” is used to mean the interrelation of conventional metadata and social tags, and their interaction with the environment, which can be understood as the repository in the large sense (resources, metadata, interfaces and underlying technology) and its community of users. The main hypothesis is that the self-organisation aspect of a social tagging system on a learning resource portal helps users discover learning resources more efficiently. Moreover, user-generated tags make the system, which operates in a multilingual context, more robust and flexible. Social tags offer an interesting aspect to study learning resources, its metadata and how users interact with them in a multilingual context. Tags, as opposed to conventional metadata description such as Learning Object Metadata (LOM), are free, non-hierarchical keywords that end-users associate with a digital artefact, e.g. a learning resource. Tags are formed by a triple of (user,item,tag). Tags and the resulting networks, folksonomies, are commonly modelled as tri- partite hypergraphs. This ternary relational structure gives rise to a number of novel relations to better understand, capture and model contextual information. This thesis first provides two exploratory studies to better understand how users tag learning resources in a multilingual context and to find evidence on the “cross-boundary use” of learning resources. The term cross-boundary use means that the user and the resource come from different countries and that the language of the resource is different from that of the user’s mother tongue. The second part introduces a trilogy of studies focusing on self-organisation, flexibility and robustness of a social tagging system using empirical, behavioural data captured from log-files and user’s attention metadata trails on a number of learning resource portals and platforms in a multilingual context.