The Usefulness of Log Based Clustering in a Complex Simulation Environment

  • Samad Kardan
  • Ido Roll
  • Cristina Conati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)


Data mining techniques have been successfully employed on user interaction data in exploratory learning environments. In this paper we investigate using data mining techniques for analyzing student behaviors in an especially-complex exploratory environment, with over one hundred possible actions at any given point. Furthermore, the outcomes of these actions depend on their context. We propose a multi-layer action-events structure to deal with the complexity of the data and employ clustering and rule mining to examine student behaviors in terms of learning performance and effects of different degrees of scaffolding. Our findings show that using the proposed multi-layer structure for describing action-events enables the clustering algorithm to effectively identify the successful and unsuccessful students in terms of learning performance across activities in the presence or absence of external scaffolding. We also report and discuss the prominent behavior patterns of each group and investigate short term effects of scaffolding.


Educational Data Mining Clustering Scaffolding 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Samad Kardan
    • 1
  • Ido Roll
    • 2
  • Cristina Conati
    • 1
  1. 1.Department of computer ScienceUniversity of British ColumbiaCanada
  2. 2.Centre for Teaching, Learning, and TechnologyUniversity of British ColumbiaVancouverCanada

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