Assessing Argumentative Representation with Bayesian Network Models in Debatable Social Issues

  •  Zhidong Zhang    
  •  Jingyan Lu    


This study seeks to obtain argumentation models, which represent argumentative processes and an assessment structure in secondary school debatable issues in the social sciences. The argumentation model was developed based on mixed methods, a combination of both theory-driven and data-driven methods. The coding system provided a combing point by which the theoretical framework and data integrated into the model. Bayesian networks were used to assess and update student progress. The study examined how to explore argumentation knowledge structure and argumentative skills as a whole via data analysis and theoretical considerations. Effective argumentative representation is a crucial step to bridge argumentative learning and assessment.

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