LinkedUp

Linking Web Data for Education Project - Open Challenge in Web-scale Data Integration

LinkedUp aims to push forward the exploitation of the vast amounts of public, open data available on the Web, in particular by educational institutions and organizations.

This will be achieved by identifying and supporting highly innovative large-scale Web information management applications through an open competition (the LinkedUp Challenge) and dedicated evaluation framework. The vision of the LinkedUp Challenge is to realise personalised university degree-level education of global impact based on open Web data and information. Drawing on the diversity of Web information relevant to education, ranging from Open Educational Resources metadata to the vast body of knowledge offered by the Linked Data approach, this aim requires overcoming substantial challenges related to Web-scale data and information management involving Big Data, such as performance and scalability, interoperability, multilinguality and heterogeneity problems, to offer personalised and accessible education services. Therefore, the LinkedUp Challenge provides a focused scenario to derive challenging requirements, evaluation criteria, benchmarks and thresholds which are reflected in the LinkedUp evaluation framework. Information management solutions have to apply data and learning analytics methods to provide highly personalised and context-aware views on heterogeneous Web data.

Building on the strong alliance of institutions with expertise in areas such as open Web data management, data integration and Web-based education, key outcomes of LinkedUp include a general-purpose evaluation framework for Web-data driven applications, a set of quality-assured educational datasets, innovative applications of large-scale Web information management, community-building and clustering crossing public and private sectors and substantial technology transfer of highly innovative Web information management technologies.

Partners

• Gottfried Wilhelm Leibniz Universit�t Hannover
• Open Knowledge Foundation Network
• Elsevier B.V.
• The Open Universiteit Nederland
• eXact learning solutions S.p.A.

Mathieu d'Aquin

Mouromtsev, D. and d'Aquin, M. (eds.) (2016) Open Data for Education: Linked, Shared, and Reusable Data for Teaching and Learning Open Data for Education: Linked, Shared, and Reusable Data for Teaching and Learning, Springer

d'Aquin, M. (2016) On the Use of Linked Open Data in Education: Current and Future Practices Open Data for Education: Linked, Shared, and Reusable Data for Teaching and Learning, eds. Dmitry Mouromtsev and Mathieu d'Aquin

Zerr, S., d'Aquin, M., Marenzi, I., Taibi, D., Adamou, A. and Dietze, S. (2014) Towards Analytics and Collaborative Exploration of Social and linked Media for Technology-Enchanced Learning Scenarios, Workshop: 1st International Workshop on Dataset PROFIling & fEderated Search for Linked Data (PROFILES 2014), Heraklion, Greece, 1151, 3, CEUR-WS

d'Aquin, M., Dietze, S., Drachsler, H., Guy, M., Herder, E. and Parodi, E. (2014) Building the Open Elements of an Open Data Competition, D-Lib Magazine, 20, 5/6

Domingue, J., d'Aquin, M., Simperl, E. and Mikroyannidis, A. (2014) The Web of Data: Bridging the Skills Gap, IEEE Intelligent Systems, 29, 1, pp. 70-74