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    认知发展理论视角下知识建构社区中的 知识进化研究

    Research on Knowledge Evolution in Knowledge Construction Community from the Perspective of Cognitive Development Theory

【作      者】:

吴 磊,赵 琪,卢萌萌,王新华


【关 键 词 】:

知识建构; 知识进化; 社会网络分析; 认知发展理论; 领域概念


【栏      目】:

学习环境与资源


【中文摘要】:

知识经济时代,以观点为中心的知识建构社区极大地促进了知识创造活动的发生。知识进化是意义学习的必然过程,为揭示知识建构社区学习本质提供了重要视窗。然而,现有研究虽然尝试从知识流动、观点演化以及知识网络等方面探讨知识建构社区的学习规律,但未能将交互内容与内在心理活动有机统一。为此,研究首先融合认知发展理论与社会网络属性,确定图式的基本单元以及知识进化框架。其次,利用数据挖掘方法抽取关键领域概念表示图式单元,通过社会网络分析和LDA主题模型,结合发帖规律深入探究不同主体与不同主题的知识进化轨迹。研究结果发现:知识建构社区中关键领域概念具有鲜明的语义特征,不同学习者的知识进化层次具有差异性,且多样化主题下知识进化规律呈现复杂性。研究拓展了传统学习理论边界,同时对优化在线教学具有重要意义。


【英文摘要】:

In the era of knowledge economy, the idea-centered knowledge construction community greatly facilitates knowledge creation. Knowledge evolution is an inevitable process of meaningful learning, which provides an important window for uncovering the nature of learning in knowledge construction community. However, although the existing research have attempted to explore the learning patterns of knowledge construction community in terms of knowledge flow, viewpoint evolution, and knowledge network, they have failed to organically unify the interactive content with the intrinsic mental activities. Therefore, this study first integrates cognitive development theory and social network properties to determine the basic unit of schema as well as the framework of knowledge evolution. Secondly, data mining methods are used to extract the key domain concepts to represent the schema unit, and through social network analysis and the LDA topic model, the knowledge evolution trajectories of different subjects and topics are deeply explored combined with the posting rule. The results show that the key domain concepts in knowledge construction community possess distinct semantic features, the knowledge evolution levels of different learners are different, and the knowledge evolution laws under diverse topics are complext. The study not only expands the boundaries of traditional learning theories but also has important implication for optimizing online teaching.

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