LILE2018 – Learning & Education with Web Data

– collocated with 10th ACM Conference on Web Science, Amsterdam, 27 May 2018


  • Proceedings now available here
  • Draft programme available
  • Keynote by Inge Molenaar (Behavioural Science Institute, Radboud University, NL) confirmed
  • Submission deadline extended to 04 April 2018
  • irst keynote speaker announced: John Domingue (KMI, The Open University, UK) will discuss the use of blockchains in educational ecosystems at LILE2018


Building on the previous editions and its growing community, LILE2018 will provide an interdisciplinary forum to discuss approaches making use of Web Data for teaching, learning and education. Distance teaching and openly available educational resources on the Web are becoming common practices with public higher education institutions as well as private training organizations. In addition, informal learning and knowledge exchange are inherent to the daily online interactions, when searching the Web or using learning and knowledge-related social networks, such as Bibsonomy, Slideshare, Wikipedia or Videolectures, or general purpose social environments, such as LinkedIn, where matters related to skills, competence development or training are central concerns of involved stakeholders. These interactions generate a vast amount of data, about informal knowledge resources of varying granularity as well as user activities, including informal indicators for learning and competences.

On the other hand, the prevalence of entity-centric Web data, facilitated through Open Data, Knowledge Graphs or Linked Data, as well as the more recent widespread adoption of embedded annotations through, Microdata and RDFa has led to the availability of vast amounts of semi-structured data which facilitates interpretation and reuse of Web content and data in learning scenarios.

Initiatives such as LinkedUp or the more recent AFEL project2 have already made available collections of learning-related data, covering both user activity as well as resource-centric information. The widespread analysis of both informal and formal learning activities and resources has the potential to fundamentally aid and transform the production, recommendation and consumption of learning services and content. Typical scenarios include the use of machine learning to automatically classify learning performance, competences or user knowledge by learning from the vast amounts of available data or to exploit resource-centric data and knowledge graphs to automatically generate learning resources or assessment items.

However, interpreting learning activities and online interactions requires a highly interdisciplinary skillset, involving knowledge about learning theory, psychology and sociology as well as technical means to enable data analysis in large-scale heterogeneous data.

Building on the success of several previous editions, LILE2018 aims at addressing such challenges by providing a forum for researchers and practitioners who make innovative use of Web data for educational purposes, spanning areas such as learning analytics, Web mining, data and Web science, psychology and the social sciences. LILE2018 will also feature an open data competition that will continue the efforts of the LinkedUp Challenge, bringing in the perspective of the AFEL project.





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