A group intelligence-based asynchronous argumentation learning-assistance platform

Abstract

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.

Keywords:

• asynchronous argumentation
• text categorization
• one-class classifier
• group intelligence
• computer-supported collaborative learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Chenn-Jung Huang received the B.S. degree in electrical engineering from National Taiwan University, Taiwan and the M.S. degree in computer science from University of Southern California, Los Angeles, in 1984 and 1987. He received the Ph.D. degree in electrical engineering from National Sun Yat-Sen University, Taiwan, in 2000. He is currently a Professor in the Department of Computer Science & Information Engineering, National Dong Hwa University, Taiwan. His research interests include e-learning, intelligent learning agents, and data mining.

Shun-Chih Chang is pursuing a Ph.D. degree at the Department of Computer Science & Information Engineering, National Dong Hwa University, Taiwan. His research interests include e-learning, intelligent learning agents, and data mining.

Heng-Ming Chen is pursuing a Ph.D. degree at the Department of Electrical Engineering, National Dong Hwa University, Taiwan. His research interests include e-learning, data mining and applications of machine learning techniques.

Jhe-Hao Tseng is pursuing a Master’s degree at the Department of Computer Science & Information Engineering, National Dong Hwa University, Taiwan. His research interests include e-learning, data mining and applications of machine learning techniques.

Sheng-Yuan Chien is pursuing a Master’s degree at the Department of Computer Science & Information Engineering, National Dong Hwa University, Taiwan. His research interests include e-learning, data mining and applications of machine learning techniques.