In Conjunction with 11th 3PGCIC-2016 Conference


Data Analysis is a cornerstone of online learning environments. Since the first conception of e-learning and collaborative systems to support learning and teaching, data analysis has been employed to support learners, teachers, researchers, managers and policy makers with useful information on learning activities and learning design. While data analysis originally employed mainly statistical techniques due to the modest amounts and varieties of data being gathered, with the rapid development of internet technologies and increasingly sophisticated online learning environments, increasing volumes and varieties of data are being generated and data analysis has moved to more complex analysis techniques such as educational data mining and, most recently, learning analytics. Now powered by cloud technologies, online learning environments are capable of gathering and storing massive amounts of data of various formats, and tracking user-system and user-user interactions as well as rich contextual information in such systems. This has led to the need to address the definition, modelling, development and deployment of sophisticated learning services offering analytics and context awareness information to all participants and stakeholders in online learning. This workshop seeks original research contributions in analytics and context awareness in learning systems, driven by service-based architectures and cloud technologies.

The workshop seeks original contributions in all relevant areas, including but not limited to the following topics. Authors of selected papers will be invited to extend their papers for publication in a new Springer Book "Software Data Engineering for Network eLearning Environments" within the series "Lecture Notes in Data Engineering and Communication Systems" (book proposal accepted by Springer).

Topics of interest

  • Learning analytics services
  • Awareness services for learners and teachers
  • Analytics and awareness services for group learning
  • Modelling knowledge domains, learner modelling
  • Interaction analysis
  • Event-based awareness services
  • Event detection, processing and semantic enrichment
  • Data querying, filtering, transformation, integration, visualisation
  • Scalable data mining for analytics and awareness
  • Services for large-scale text analysis and mining
  • Description and composition of learning services
  • Schema and metadata models for indexing services
  • Services for metadata management, provenance, trust
  • Analytics for Massive Open Online Courses (MOOCs)
  • Cloud, P2P and Social Networking for learning services
  • Emerging trends in learning analytics and awareness services
  • Performance metrics, benchmarks and data sets
  • Evaluation methodologies
  • Case studies and applications