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Curriculum Optimization by Correlation Analysis and Its Validation

  • Kohei Takada
  • Yuta Miyazawa
  • Yukiko Yamamoto
  • Yosuke Imada
  • Setsuo Tsuruta
  • Rainer Knauf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7947)

Abstract

The paper introduces a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. The technology is a ”lazy” Data Mining technology, which models students’ learning characteristics by considering real data instead of deriving (”guessing”) their characteristics explicitly. It aims at mining course characteristics similarities of former students’ study traces and utilizing them to optimize curricula of current students based to their performance traits revealed by their educational history. This (compared to a former publication) refined technology generates suggestions of personalized curricula. The technology is supplemented by an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.). Finally, the paper derives some refinement ideas for the evaluation method.

Keywords

adaptive learning technologies personalized curriculum mining educational data mining 

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References

  1. 1.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Engineering Education 78(7), 674–681 (1988)Google Scholar
  2. 2.
    Gardner, H.: Frames of Mind: The Theory of Multiple Intelligences. Basic Books, New York (1993)Google Scholar
  3. 3.
    Knauf, R., Sakurai, Y., Takada, K., Dohi, S.: Personalized Curriculum Composition by Learner Profile Driven Data Mining. In: Proc. of the 2009 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2009), San Antonio, TX, USA, pp. 2137–2142 (2009) ISBN: 978-1-4244- 2794-9Google Scholar
  4. 4.
    Knauf, R., Sakurai, Y., Tsuruta, S., Jantke, K.P.: Modeling Didactic Knowledge by Storyboarding. Journal of Educational Computing Research 42(4), 355–383 (2010) ISSN: 0735-6331 (Paper) 1541-4140 (Online)CrossRefGoogle Scholar
  5. 5.
    Tsuruta, S., Knauf, R., Dohi, S., Kawabe, T., Sakurai, Y.: An Intelligent System for Modeling and Supporting Academic Educational Processes. In: Peña-Ayala, A. (ed.) Intelligent and Adaptive ELS. SIST, vol. 17, pp. 469–496. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kohei Takada
    • 1
  • Yuta Miyazawa
    • 1
  • Yukiko Yamamoto
    • 1
  • Yosuke Imada
    • 1
  • Setsuo Tsuruta
    • 1
  • Rainer Knauf
    • 2
  1. 1.Tokyo Denki UniversityInzaiJapan
  2. 2.Ilmenau University of TechnologyIlmenauGermany

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