This paper introduces a new learning control method – ‘value-added mode’. This mode is based on counting credits for only new knowledge learned whereas ‘old’ knowledge is taken into account with low weight. The need for such mode appears when background of students starting a course is very varying. This situation becomes more and more frequent, because of globalization, personal study tracks etc. In this paper we describe how this mode is implemented and also describe an application Build-Your-Course.


Course Adaptive Credits Compiling course 


  1. 1.
    De Urquidi, K., Verdin, D., Hoffmann, S., Ohland, M.W.: Outcomes of accepting or declining advanced placement calculus credit. In: Frontiers in Education Conference (FIE), pp. 1–6. IEEE (2015)Google Scholar
  2. 2.
    Rasch, G.: On general laws and the meaning of measurement in psychology. In: Proceedings of Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 4, pp. 321–333. University of California Press (1961)Google Scholar
  3. 3.
    Kaur, K. Kaur, K.: Analyzing the effect of difficulty level of a course on students performance prediction using data mining. In: Proceedings of 1st International Conference on Next Generation Computing Technologies, Dehradun, India, pp. 756–761 (2015)Google Scholar
  4. 4.
    Tao, J., Wu, G.: An application of undergraduate academic growth path on the credit system based on data mining. In: Proceedings of 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, pp. 789–794 (2015)Google Scholar
  5. 5.
    Fernandez, A., Delgado, E., Montoya, Y., Gonzalez, R., Vaughan, M.: Student led curriculum development and instruction of introduction to engineering leadership course. In: Frontiers in Education Conference (FIE), pp. 1–8. IEEE (2015)Google Scholar
  6. 6.
    Wang, M., Zheng, J., Wang, S.: A new automatic knowledge extraction method for course documents applied in the web-based teaching system. In: Proceedings of 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, pp. 1620–1625 (2015)Google Scholar
  7. 7.
    Wozniak, P.A.: Two components of long-term memory. Acta Neurobiol. Exp. 55, 301–305 (1994)Google Scholar
  8. 8.
    Wixted, J.T., Carpenter, S.K.: The Wickelgren power law and the ebbinghaus savings function. Psychol. Sci. 18(2), 133–134 (2007)CrossRefGoogle Scholar
  9. 9.
    Kukk, V., Umbleja, K.: Analysis of forgetting in a learning environment. In: 13th Biennial Baltic Electronics Conference, Tallinn, Estonia, pp. 335–338, 3–5 October 2012Google Scholar
  10. 10.
    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
  11. 11.
    Kukk, V., Umbleja, K., Jaanus, M.: Two-dimensional knowledge model for learning control and competence mapping. In: Uden, L., Liberona, D., Welzer, T. (eds.) LTEC 2015. CCIS, vol. 533, pp. 16–27. Springer, Heidelberg (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tallinn University of TechnologyTallinnEstonia

Personalised recommendations