A group intelligence-based asynchronous argumentation learning-assistance platform

Structured argumentation support environments have been built and used in scientific discourse in the literature. However, to the best our knowledge, there is no research work in the literature examining whether student’s knowledge has grown during learning activities with asynchronous argumentation. In this work, an intelligent computer-supported collaborative argumentation-based learning platform that detects whether the learners address the expected discussion issues is proposed. After each learner presents an argument, a term weighting method is adopted to derive input parameters of a one-class support vector machines classifier which determines if the learners’ arguments are related to the discussion topics. Notably, a peer review mechanism is established to improve the quality of the classifier. Besides, a feedback module is used to issue feedback messages to the learners if the learners have gone off on a tangent. The experimental results revealed that the students were benefited by the proposed learning-assistance platform.