Abstract—In this work, an asynchronous learning platform that detects whether the learners address the expected discussion issues on asynchronous discussion boards is proposed. Concept maps related to the learning topics are first outlined by the instructor. After each learner presents a post on the asynchronous learning platform, a term weighting method is adopted to derive input parameters of a Support Vector Machines (SVMs) classifier. The classifier then determines if the learners’ posts are related to the discussion topics. Notably, a peer review mechanism based on group intelligence is established in this work to improve the quality of the classifier. At the same time, a feedback module is used to issue feedback messages to the learners in cases where the proposed asynchronous discussion board detects that the learners have gone off on a tangent. The experimental results revealed that the 31 students in a junior high school participating in asynchronous online discussion activities related to natural science were benefited by the proposed learning assistance platform.

Index Terms—Asynchronous discussion board, computer -supported collaborative learning, group intelligence, SVM classifier, text categorization.

The authors are with National Dong Hwa University, Taiwan (e-mail: cjhuang@mail.ndhu.edu.tw).

Cite:Chenn-Jung Huang, Shun-Chih Chang, Heng-Ming Chen, Chao-Yi Chen, Jhe-Hao Tseng, and Sheng-Yuan Chien, "An Intelligent Assessment Mechanism of Asynchronous Discussion Posts," International Journal of Computer and Communication Engineering vol. 4, no. 1, pp. 66-72, 2015.