Learning analytics refers to the process of collecting, analyzing, and visualizing (large scale) data about learners for the purpose of understanding and pro-actively optimizing teaching strategies. A related concept is formative assessment – the idea of drawing information about a learner from a broad range of sources and on a competence-centered basis in order to go beyond mere grading to a constructive and tailored support of individual learners. In this paper we present an approach to competence-centered learning analytics on the basis of so-called Competence-based Knowledge Space Theory and a way to visualize learning paths, competency states, and to identify the most effective next learning steps using Hasse diagrams.


Learning analytics data visualization Hasse diagram Competencebased Knowledge Space Theory 


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  1. 1.
    Holzinger, A.: On Knowledge Discovery and interactive intelligent visualization of biomedical data - Challenges in Human–Computer Interaction & Biomedical Informatics. In: Helfert, M., Francalanci, C., Filipe, J. (eds.) Proceedings of the International Conference on Data Technologies and Application, DATA 2012, Rome, pp. 3–16. SciTec Press, Setubal (2012)Google Scholar
  2. 2.
    Holzinger, A., Yildirim, P., Geier, M., Simonic, K.-M.: Quality-based knowledge discovery from medical text on the Web Example of computational methods in Web intelligence. In: Pasi, G., Bordogna, G., Jain, L.C. (eds.) Qual. Issues in the Management of Web Information. ISRL, vol. 50, pp. 145–158. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Buckingham Shum, S., Ferguson, R., Duval, E., Verbert, K., Baker, R.S.J.D.: Open Learning Analytics: an integrated & modularized platform: Proposal to design, implement and evaluate an open platform to integrate heterogeneous learning analytics techniques (2011),
  4. 4.
    Ferguson, R., Buckingham Shum, S.: Social Learning Analytics: Five Approaches. In: Proceedings of the 2nd International Conference on Learning Analytics & Knowledge, Vancouver, British Columbia, Canada, April 29-May 02 (2012)Google Scholar
  5. 5.
    Duval, E.: Attention Please! Learning Analytics for Visualization and Re-commendation. In: Proceedings of the 1st International Conference on Learning Analytics & Knowledge, Banff, Alberta, Canada, February 27-March 1 (2011)Google Scholar
  6. 6.
    Dimitrova, V., McCalla, G., Bull, S.: Open Learner Models: Future Research Directions (Special Issue of IJAIED Part 2). International Journal of Artificial Intelligence in Education 17(3), 217–226 (2007)Google Scholar
  7. 7.
    Doignon, J., Falmagne, J.: Spaces for the assessment of knowledge. International Journal of Man-Machine Studies 23, 175–196 (1985)zbMATHCrossRefGoogle Scholar
  8. 8.
    Doignon, J., Falmagne, J.: Knowledge Spaces. Springer, Berlin (1999)zbMATHCrossRefGoogle Scholar
  9. 9.
    Albert, D., Lukas, J. (eds.): Knowledge Spaces: Theories, Empirical Research, and Applications. Lawrence Erlbaum Associates, Mahwah (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael D. Kickmeier-Rust
    • 1
  • Dietrich Albert
    • 1
  1. 1.Cognitive Science Section, Knowledge Management InstituteGraz University of TechnologyGrazAustria

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