Educational linked data on the web - exploring and analysing the scope and coverage

Throughout the last few years, the scale and diversity of datasets published according to Linked Data (LD) principles has increased and also led to the emergence of a wide range of data of educational relevance. However, sufficient insights into the state, coverage and scope of available educational Linked Data seem still missing. In this work, we analyse the scope and coverage of educational linked data on the Web, identifying the most significant resource types and topics and apparent gaps. As part of our findings, results indicate a prevalent bias towards data in areas such as the life sciences as well as computing-related topics. In addition, we investigate the strong correlation of resource types and topics, where specific types have a tendency to be associated with particular types of categories, i.e. topics. Given this correlation, we argue that a dataset is best understood when considering its topics, in the context of its specific resource types. Based on this finding, we also present a Web data exploration tool, which builds on these findings and allows users to navigate through educational linked datasets by considering specific type and topic combinations.