Implementation and Performance Evaluation of an Intelligent Online Argumentation Assessment System

Recent researches indicated that students´ ability to construct evidence based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best our knowledge, no research work in the literature addressed the issue of automatically assessing the student´s argumentation quality. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners´ arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in the biased direction. The experimental results exhibit that the proposed work is effective in classifying each student´s argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.