A multi-agent model that promotes team-role balance in computer supported collaborative learning
ADNTIIC'11 Proceedings of the Second international conference on Advances in New Technologies, Interactive Interfaces and Communicability
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The purpose of this paper is to show how data mining may offer promise as a strategy for discovering learner's roles in CSCL. At present, there are many researchers who focus on analyzing learner roles based on collaborative learning activities. But most of them subjectively presume the diversity of learners’ roles in advance and then verify it based on the statistics of interaction data. In contrast, this paper adopts the data mining technology to explore learners’ roles in collaborative learning. Referring to the typical process of data mining, this paper proposes a framework of role analysis based on Data Mining which data preparation and learner discourse pattern mining are depicted. Furthermore, a case study is conducted to show the mining process and finding as well as discussion on the mining results.