Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning

Volume 408 of the series Studies in Computational Intelligence pp 295-310

Validating Empirically a Rating Approach for Quantifying the Quality of Collaboration

  • Georgios KahrimanisAffiliated withHuman-Computer Interaction Group, University of Patras Email author 
  • , Irene-Angelica ChountaAffiliated withHuman-Computer Interaction Group, University of Patras
  • , Nikolaos AvourisAffiliated withHuman-Computer Interaction Group, University of Patras

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Interdisciplinarity in the Computer Supported Collaborative Learning (CSCL) research field involves the application of several methodological approaches towards analysis that range from deep-level qualitative analyses of small interaction-rich episodes of collaboration, to quantitative measures of suitably categorized events of interaction used as indicators of the success of collaboration in some of its facets. This article adopts an alternative approach to CSCL analysis that aims at taking advantage of some desired properties of each of these diverse methodological trends, involving the use of a rating scheme for the assessment of collaboration quality. After defining a set of dimensions that cover the most important aspects of collaboration, it employs appropriately trained human raters basing their assessments on substantial aspects of collaboration that are not easily formalisable. The activities studied here regard 228 collaborating dyads, working synchronously on a problem-solving task. Based on this large dataset, relations between dimensions of collaboration quality are unraveled on empirical grounds, by elaborating ratings statistically using a multidimensional scaling technique.