Comparing the visual affordances of discrete time Markov chains and epistemic network analysis for analysing discourse connections

Abstract

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IntroductionResearchers in the learning sciences have been considering methods of analysing and representing group-level temporal data, particularly discourse analysis, in Computed Supported Collaborative Learning for many years.MethodsThis paper compares two methods used to analyse and represent connections in discourse, Discrete Time Markov Chains and Epistemic Network Analysis. We illustrate both methods by comparing group-level discourse using the same coded dataset of 15 high school students who engaged in group work. The groups were based on the tools they used namely the computer, iPad, or Interactive Whiteboard group. The aim here is not to advocate for a particular method but to investigate each method’s affordances.ResultsThe results indicate that both methods are relevant in evaluating the code connection within each group. In both cases, the techniques have supported the analysis of cognitive connections by representing frequent co-occurrences of concepts in a given segment of discourse.DiscussionAs the affordances of both methods vary, practitioners may consider both to gain insight into what each technique can allow them to conclude about the group dynamics and collaborative learning processes to close the loop for learners.

Keywords

• visualisation
• collaboration analytics
• discrete time Markov chains
• epistemic network analysis
• boundary negotiating objects