Automatic Recognition of Learner Groups in Exploratory Learning Environments

  • Saleema Amershi
  • Cristina Conati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


In this paper, we present the application of unsupervised learning techniques to automatically recognize behaviors that may be detrimental to learning during interaction with an Exploratory Learning Environment (ELE). First, we describe how we use the k-means clustering algorithm for off-line identification of learner groups with distinguishing interaction patterns who also show similar learning improvements with an ELE. We then discuss how a k-means on-line classifier, trained with the learner groups detected off-line, can be used for adaptive support in ELEs. We aim to show the value of a data-based approach for recognizing learners as an alternative to knowledge-based approaches that tend to be complex and time-consuming even for domain experts, especially in highly unstructured ELEs.


Constraint Satisfaction Problem Learner Group Automatic Recognition Pause Duration Adaptive Support 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Saleema Amershi
    • 1
  • Cristina Conati
    • 1
  1. 1.Dept. of Computer ScienceUniversity of British ColumbiaVancouverCanada

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