可演化的个人知识图建构及认知评估模型研究
发布时间：2019-04-03 20:35
【摘要】：知识管理最重要的一个方面就是如何将学习者已经学过的知识具体化和可视化。尽管思维导图、本体以及知识图表是目前最流行的用来诠释和组织知识的方法与工具,但是这些方法与工具在模拟学习者的知识碎片组建上还存在缺陷,尤其是缺少必要的复杂语义推理支持机制和通过个人知识图产生的相关认知模拟。为模拟真实的个人知识碎片组建过程,我们设计并实现了一个可采集并集成个人显性知识碎片的自规律在线学习系统,以及融入了知识整合认知机制的个体隐性知识完成度评估模型。自规律在线学习系统,可构成个人知识语义链接图(SLN),将个人生成的显性知识碎片动态呈现给学习者;个体隐性知识完成度评估模型,可以可视化地呈现个人隐性知识碎片的动态演化轨迹,同时以较高保真度还原学习者知识认知程度的周期性变化曲线。实验也初步验证了学习者个人知识图的动态演化能力以及个体隐性知识完成度评估模型的保真度。
[Abstract]:One of the most important aspects of knowledge management is how to concretize and visualize what learners have learned. Although mind maps, ontologies, and knowledge charts are currently the most popular methods and tools for interpreting and organizing knowledge, these methods and tools still have shortcomings in the generation of knowledge fragments that simulate learners' knowledge. In particular, there is a lack of necessary support mechanisms for complex semantic reasoning and related cognitive simulations generated through personal knowledge maps. In order to simulate the real formation process of personal knowledge fragments, we design and implement a self-regulation online learning system which can collect and integrate personal dominant knowledge fragments. And the evaluation model of individual tacit knowledge completion degree, which is integrated with the cognitive mechanism of knowledge integration. The self-regular online learning system can constitute the semantic linkage graph of personal knowledge, (SLN), which can dynamically present the explicit knowledge fragments generated by individuals to learners. The evaluation model of individual tacit knowledge completion can visualize the dynamic evolution track of individual tacit knowledge fragments and restore the periodic curve of learners' knowledge cognition with higher fidelity. The experiment also verifies the dynamic evolution ability of individual knowledge map and the fidelity of individual tacit knowledge completion evaluation model.
【作者单位】： 四川师范大学计算机科学学院;
【基金】：国家自然科学基金“智能学习环境中的学习风格动态预测模型及其应用研究”(61402309);国家自然科学基金“基于交互行为感和MOOC学习导引机制研究”(61602331) 四川省重点实验室开放课题“MOOC环境下的协同学习关键技术研究”(NDSMS201606)
【分类号】：G434
本文编号：2453555
[Abstract]:One of the most important aspects of knowledge management is how to concretize and visualize what learners have learned. Although mind maps, ontologies, and knowledge charts are currently the most popular methods and tools for interpreting and organizing knowledge, these methods and tools still have shortcomings in the generation of knowledge fragments that simulate learners' knowledge. In particular, there is a lack of necessary support mechanisms for complex semantic reasoning and related cognitive simulations generated through personal knowledge maps. In order to simulate the real formation process of personal knowledge fragments, we design and implement a self-regulation online learning system which can collect and integrate personal dominant knowledge fragments. And the evaluation model of individual tacit knowledge completion degree, which is integrated with the cognitive mechanism of knowledge integration. The self-regular online learning system can constitute the semantic linkage graph of personal knowledge, (SLN), which can dynamically present the explicit knowledge fragments generated by individuals to learners. The evaluation model of individual tacit knowledge completion can visualize the dynamic evolution track of individual tacit knowledge fragments and restore the periodic curve of learners' knowledge cognition with higher fidelity. The experiment also verifies the dynamic evolution ability of individual knowledge map and the fidelity of individual tacit knowledge completion evaluation model.
【作者单位】： 四川师范大学计算机科学学院;
【基金】：国家自然科学基金“智能学习环境中的学习风格动态预测模型及其应用研究”(61402309);国家自然科学基金“基于交互行为感和MOOC学习导引机制研究”(61602331) 四川省重点实验室开放课题“MOOC环境下的协同学习关键技术研究”(NDSMS201606)
【分类号】：G434
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