Applications of data mining to an online argumentation based learning assistance platform

In this work, an intelligent online argumentation platform that detects whether the learners address the expected discussion issues is proposed. The concept maps related to the learning topics are first outlined by the instructor. After each learner issues an argument on the learning platform, a term weighting method is adopted to derive inputs parameters of a Support Vector Machines (SVMs) classifier. The classifier then determines if the learners´ arguments are related to the concept maps outlined by the instructor. Notably, a peer review mechanism is established in this work to improve the quality of term weighting approach. The experimental results revealed that the students in a junior high school participating in argumentation learning activities of natural science were benefited by the proposed argumentation based learning assistance platform.