Detecting and Solving Negative Situations in Real CSCL Experiences with a Role-Based Interaction Analysis Approach

  • José Antonio Marcos-García
  • Alejandra Martínez-Monés
  • Yannis Dimitriadis
  • Rocío Anguita-Martínez
  • Inés Ruiz-Requies
  • Bartolomé Rubia-Avi
Part of the Studies in Computational Intelligence book series (SCI, volume 246)


Collaborative learning has a number of potential benefits, which do not always occur, partially due to the difficulties that students and teachers have to establish good social interaction patterns. These interaction patterns depend on the roles assumed by participants in the learning process. In real practice, teachers need support to be able to detect these emergent roles and undesired interaction patterns, especially if collaboration is mediated by computers, and thus is not directly observable by humans. Interaction analysis (IA) methods and tools are adequate to support the regulation of the collaborative activities, using the analysis results to provide adequate feedback to the different participants in their specific roles. We have proposed a role-based approach supported by a tool called Role-AdaptIA to detect and help to solve problematic situations in authentic computer supported collaborative learning (CSCL) scenarios. Role-AdaptIA is an adaptive interaction analysis tool based on a theoretical framework for the description of roles. The framework permits to define and characterize the roles to take into account in a given situation. Based on this information, Role-AdaptIA automatically detects role changes during the development of the collaborative experiences and warns the teacher about these changes. With this advice, the teacher is able to regulate the collaboration, providing support to the students to improve their interaction patterns. This chapter presents four examples of how Role-AdaptIA was used by teachers in order to regulate collaboration, detecting and solving undesired collaborative situations in several University courses where we have applied CSCL methods during the last years.


Collaborative Learning Social Network Analysis Betweenness Centrality Collaborative Activity Computer Support Collaborative Learn 
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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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • José Antonio Marcos-García
    • 1
  • Alejandra Martínez-Monés
    • 1
  • Yannis Dimitriadis
    • 2
  • Rocío Anguita-Martínez
    • 3
  • Inés Ruiz-Requies
    • 3
  • Bartolomé Rubia-Avi
    • 3
  1. 1.School of Computer Science Engineering 
  2. 2.School of Telecommunications Engineering 
  3. 3.Faculty of EducationUniversity of ValladolidValladolidSpain

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