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Performance evaluation of an online argumentation learning assistance agent

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Computers & Education Volume 57, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd


Recent research 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 of our knowledge, no research work in the literature addressed the issue of automatically assessing the student’s argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. 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. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. 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 a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student’s argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.


Huang, C.J., Wang, Y.W., Huang, T.H., Chen, Y.C., Chen, H.M. & Chang, S.C. (2011). Performance evaluation of an online argumentation learning assistance agent. Computers & Education, 57(1), 1270-1280. Elsevier Ltd. Retrieved November 15, 2019 from .

This record was imported from Computers & Education on January 29, 2019. Computers & Education is a publication of Elsevier.

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