Big Data e-Infrastructure for Educational Data (SLATE)

Project Department: Uni Research Computing (group: Center for Big Data Analysis)

About the project

This project is part of the cluster "BIG and SMALL DATA in Education" established in the research center SLATE. Uni Research Computing will carry out the realization of a Big Data e-infrastructure to accompany the scientific activities of SLATE. The e-infrastructure will be the host of raw primary educational data and will support its access, assessment, analysis, comparison and manipulation. Tools will be developed in close collaboration with the researchers of SLATE. Further tools are provided for other stakeholders in the Educational sector, such as public authorities, teachers and learners, which receive services based on state-of-the-art scientific insights.

To support the communication with the related external communities, the e-infrastructure will be equipped with machine-2-machine (M2M) interfaces to enable the communication with external datasources, such as the University of Bergen's (UiB) Mitt UiB e-support platform, MOOC-systems nationally and internationally, other e-learning platforms and learning applications e.g., open educational resources. Collaboration with other LAK-centers [1] on international level is envisaged. Further, the center will provide graphical user-interfaces (web-portals) to support directly human access to the knowledge system. Different roles for public authorities, teachers and learners as well as external researchers will be carefully designed to allow reporting/interaction on the scientific, macro, medium or micro-level.

On the systems level, the knowledge system will be realized by means of a Big Data data-lake, storing raw educational data and other education related information. The system will provide carefully designed Machine Learning algorithms to support scientific and service tasks. The hierarchy of raw data and several levels of derived data will be established throughout the lifetime of SLATE along the lines of the center’s research activities. The system will be based on a fully scalable Big Data platform and will be able to host all Norwegian educational data as well as its analysis, if the need arises.
In the first project year, the system will create an import-interface to an initial set of UiB data (such as Mitt UiB learning platform) and appropriate internal representations. During the first ~2 years of the project analogous import-functionalities and representations for other external data sources, such as the FutureLearn platform and other sources, will be realized. Analysis tools for researchers, basic and advanced visualization capabilities will be implemented during later times in the project, see the milestones given below. The activities will be accompanied by the IT-operations, maintenance and user support activities over the entire lifetime of the project.


  • IT-operations, maintenance and user support
  • Initial set of UiB data (e.g. Mitt UiB) import and representation
  • Future Learn (MOOC) and further import and representation
  • Scientific data analysis tools
  • Basic dashboards and visualisations
  • Dashboards and visualizations for multiple roles


  • Big Data e-Infrastructure for Educational Data (has the potential to be used for all Norwegian educational data)
  • Learning Analysis platform that uses the Open Learner Model (OLM) & various Learning Analysis and Educational Data Mining techniques
  • Scientific data analysis tools
  • Basic dashboards and visualisations
  • Dashboards and visualizations for multiple roles


[1] LAK: Leaning Analytics and Knowledge

further material on SLATE:


cp: 2019-12-04 11:17:27