Study on contexts in tracking usage and attention metadata in the field of multilingual Technology Enhanced Learning

In order to exploit usage and attention metadata, one needs to define what properties of usage to use and why, second, how to gather data on that property, and third, what to do with that information. In this paper, we propose to consider usage and attention metadata as an example of the wider notion of context and give an overview of dimensions of context that are relevant in technology enhanced learning (TEL). We consider the intersection of the areas of digital learning resource repositories, digital libraries, and other Web environments including social tagging systems. Specifically, we argue that context comprises the usage situation and environment as well as persistent and transient properties of the user. Therefore, we distinguish between the macro-context and the micro-context of TEL. We further subdivide the latter into user models, material/environment models, interaction models, and background knowledge, showing that usage and attention metadata are of different types and play different roles for learning about context. We then concentrate on teachers using learning-resource repositories as an important use-case example of TEL and focus on language and country as context variables. We describe different ways in which these variables can be measured, i.e., ways of operationalising the construct and data gathering to provide values for the variable. Finally, we outline how TEL can use such context information to improve the use and reuse of repositories by making them more useful in a multilingual and multicultural context. A key theme of our article is the central role that social tagging can play in this process: on the one hand, tags describe usage, attention, and other aspects of context; on the other hand, they can help to exploit context data towards making repositories more useful, and thus enhance the reuse.