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Two-Dimensional Knowledge Model for Learning Control and Competence Mapping

  • Vello Kukk
  • Kadri Umbleja
  • Martin Jaanus
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 533)

Abstract

The paper presents two-dimensional model for knowledge representation with volume as one variable and ability as another one. This makes possible describing current state of learner’s abilities and integration for higher level parameters e.g. grading related to course or other entities. Both values are related to atomized knowledge elements (competences) with volume interpreted as credit units and ability levels are formed during learning with application of forgetting. This model makes possible characterization (grading) of knowledge based on real abilities independently of predeclared courses and for ‘drop-outs’. So, on that bases one can obtain grade for some course if proper knowledge has been obtained in different courses and schools even when courses had not passed. Also this model helps to build connections between courses as using courses in the role of prerequisites becomes less usable. Not wasting knowledge obtained in MOOCs is another example with high drop-out levels where classical passed-failed model does not work.

Keywords

Competence-based learning Credit units Abilities Automatic processing of solutions 

References

  1. 1.
    Gütl, C., Rizzardini, R.H., Chang, V., Morales, M.: Attrition in MOOC: lessons learned from drop-out students. In: Uden, L., Sinclair, J., Tao, Y.-H., Liberona, D. (eds.) LTEC 2014. CCIS, vol. 446, pp. 37–48. Springer, Heidelberg (2014)Google Scholar
  2. 2.
    Jordan, K.: Initial trends in enrolment and completion of massive open online courses. Int. Rev. Res. Open Distance Learn. 15(1), 133–160 (2014)Google Scholar
  3. 3.
  4. 4.
    Rozeboom, H.: Competence based learning. Bull. Univ. Agric. Sci. Vet. Med. Cluj-Napoca 63(1), 365–367 (2006)Google Scholar
  5. 5.
    Sánchez, A.V., Ruiz, M.P.: Competence Based Learning - A Proposal for the Assessment of Generic Competences, p. 29. University of Deusto, Bilbao (2008)Google Scholar
  6. 6.
    Jaanus, M.: The Interactive Learning Environment for Mobile Laboratories, p. 59. Tallinn University of Technology Press, Tallinn (2011)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Kukk, V.: Student’s behavior in free learning environment and formal education System. In: Uden, L., Sinclair, J., Tao, Y.-H., Liberona, D. (eds.) LTEC 2014. CCIS, vol. 446, pp. 187–194. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Baker, F.B.: The Basics of item Response Theory: ERIC Clearinghouse on Assessment and Evaluation. University of Maryland, College Park (2001)Google Scholar
  11. 11.
  12. 12.
    Al-A’Ali, M.: IRT-Item response theory assessment for an adaptive teaching assessment system. In: 10th WSEAS International Conference on Applied Mathematics, pp. 518–522, Dallas, USA (2006)Google Scholar
  13. 13.
    Umbleja, K.: Students’ grading control and visualisation in competence-based learning approach. In: IEEE Educon 2015, pp. 294–303 (2015)Google Scholar
  14. 14.
    Wozniak, P.A.: Two components of long-term memory. Acta Neurobiol. Exp. 55, 301–305 (1995)Google Scholar
  15. 15.
  16. 16.
    Wixted, J.T., Carpenter, S.K.: The Wickelgren power law and the Ebbinghaus savings function. Psychol. Sci. 18(2), 133–134 (2007)CrossRefGoogle Scholar
  17. 17.
    Kukk, V., Umbleja, K.: Analysis of forgetting in a learning environment. In: 13th Biennial Baltic Electronics Conference, pp. 335–338, Tallinn, Estonia, 3–5 October 2012Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ControlTallinn University of TechnologyTallinnEstonia

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