Optimizing university curricula through correlation analysis

  • Knauf R
  • Yamamoto Y
  • Sakurai Y
 et al. 
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In this paper, we introduce a refined Educational Data Mining approach, which refrains from explicit learner modeling along with an evaluation concept. We use a Data Mining technology, which models students' learning characteristics by considering real data instead of deriving 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 refined technology generates suggestions of personalized curricula. The technology includes 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 shows some pre-validation results and approaches for a final validation.

Author-supplied keywords

  • Adaptive Learning Technologies
  • Educational Data Mining
  • Personalized Curriculum Mining

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  • Rainer Knauf

  • Yukiko Yamamoto

  • Yoshitaka Sakurai

  • K. Kinshuk

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