An investigation into knowledge discovery in collaborative learning communities

Jian Liao, Minhong Wang, Yanyan Li, Ronghuai Huang
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引用次数: 1

Abstract

While collaborative community is applied comprehensively in school learning and organisational training, research on collaborative learning process is emerging. Traditional statistical methods make it difficult to perform large volume in-depth analysis on interaction regulations and mechanisms in collaborative communities, especially when bottom-up analysis is required. In this study, data-mining methodology is addressed to discover knowledge from large-scale and real interaction data in collaborative learning communities. We propose to use Knowledge Discovery in Databases (KDD) approach for knowledge discovery in collaborative communities. A case study of role discovery in collaborative learning is developed to demonstrate the usefulness of the approach.
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协作学习社区中知识发现的研究
随着协作社区在学校学习和组织培训中的广泛应用,对协作学习过程的研究正在兴起。传统的统计方法很难对协作社区中的交互规则和机制进行大量深入的分析,特别是当需要自下而上的分析时。在本研究中,数据挖掘方法被用于从协作学习社区的大规模和真实交互数据中发现知识。我们建议将知识库知识发现(Knowledge Discovery in Databases, KDD)方法用于协作社区的知识发现。本文以合作学习中角色发现的案例研究为例,论证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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