Modeling the process of collaborative learning

  • Amy Soller
  • Alan Lesgold
Part of the Computer-Supported Collaborative Learning book series (CULS, volume 9)

Abstract: Supporting group learning activities requires an understanding of the process of collaborative learning. This process is complex, coupling task-based and social elements. We present a view of the collaborative learning process from multiple perspectives, highlighting those that drive explaining, criticizing, sharing, and motivating behaviors. Modeling and supporting these processes requires a fine-grained sequential analysis of the group activity and collaboration. The selection of a computational approach to perform this analysis should take into account the chosen perspective and the desired goal: to better understand the interaction, or to provide advice or support to the students. Examples of five different computational approaches for modeling collaborative learning are discussed: Finite State Machines, Rule Learners, Decision Trees, Plan Recognition, and Hidden Markov Models. We illustrate the Hidden Markov Modeling approach in detail, showing that it performs significantly better than statistical analysis in recognizing the knowledge sharer, and the knowledge recipients when students exchange new knowledge during learning activities.


Hide Markov Model Knowledge Sharing Collaborative Learning Finite State Machine Knowledge Element 
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 Science+Business Media, LLC. 2007

Authors and Affiliations

  • Amy Soller
    • 1
  • Alan Lesgold
    • 2
  1. 1.Institute for Defense AnalysesAlexandriaUSA
  2. 2.School of EducationUniversity of PittsburghPittsburghUSA

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