Supporting contextualized learning with linked open data

TL;DR: A clustering approach is proposed that can be applied to knowledge graphs and the possibility of applying Locality Sensitive Hashing is explored, and it is observed that this approach can be effective and scalable in comparison to other clustering approaches such as Hierarchical clustering, K‐Means clustering and K‐Medoid clustering.

TL;DR: Cultural Heritage Educational Semantic Tool (CHEST), a web application that enables teachers to identify monuments of interest and annotate them with learning tasks that promotes the reusability and adaptation of learning tasks by different teachers is presented.

TL;DR: This paper presents a new model for organizing learning resources: Learning Cell, which makes up for the defects of existing learning technologies in the following areas: the sharing of process information and social cognition networks, the intelligence of resources, and the evolution of content.

TL;DR: This Synthesis lecture provides readers with a detailed technical introduction to Linked Data, including coverage of relevant aspects of Web architecture, as the basis for application development, research or further study.

TL;DR: This paper explores the origin of these limitations, and introduces an alternative and more stable agreement coefficient referred to as the AC1 coefficient, and proposes new variance estimators for the multiple-rater generalized pi and AC1 statistics, whose validity does not depend upon the hypothesis of independence between raters.