Cluster-Based Prediction of Mathematical Learning Patterns

  • Tanja Käser
  • Alberto Giovanni Busetto
  • Barbara Solenthaler
  • Juliane Kohn
  • Michael von Aster
  • Markus Gross
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7926)

Abstract

This paper introduces a method to predict and analyse students’ mathematical performance by detecting distinguishable subgroups of children who share similar learning patterns. We employ pairwise clustering to analyse a comprehensive dataset of user interactions obtained from a computer-based training system. The available data consist of multiple learning trajectories measured from children with developmental dyscalculia, as well as from control children. Our online classification algorithm allows accurate assignment of children to clusters early in the training, enabling prediction of learning characteristics. The included results demonstrate the high predictive power of assignments of children to subgroups, and the significant improvement in prediction accuracy for short- and long-term performance, knowledge gaps, overall training achievements, and scores of further external assessments.

Keywords

feature processing pairwise clustering prediction learning dyscalculia 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tanja Käser
    • 1
  • Alberto Giovanni Busetto
    • 1
    • 2
  • Barbara Solenthaler
    • 1
  • Juliane Kohn
    • 4
  • Michael von Aster
    • 3
    • 4
    • 5
  • Markus Gross
    • 1
  1. 1.Department of Computer ScienceETH ZurichZurichSwitzerland
  2. 2.Competence Center for Systems Physiology and Metabolic DiseasesZurichSwitzerland
  3. 3.Center for MR-ResearchUniversity Children’s HospitalZurichSwitzerland
  4. 4.Department of PsychologyUniversity of PotsdamPotsdamGermany
  5. 5.Department of Child and Adolescent PsychiatryGerman Red Cross Hospitals WestendBerlinGermany

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