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    认知负荷视角下在线协作学习的增效机制研究

    Study on Synergistic Mechanism of Online Collaborative Learning from A Cognitive Load Perspective

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【作      者】:

王希哲, 涂雅欣, 张琳捷, 黄琼浩


【关 键 词 】:

在线协作学习; 认知负荷理论; 协作状态量化; 演化模型; 增效机制


【栏      目】:

网络教育


【中文摘要】:

在线协作学习作为一种跨时空情境下的社会化认知学习方式,其过程状态的有效感知与活动优化对于解决群体学习认知负荷失衡、促进高质量共同发展至关重要。为此,文章以在线协作学习过程的状态分析与适性引导为目标,融合协作认知负荷理论与智能技术手段,对在线协作学习过程量化方法与协作增效机制予以实现与验证。研究首先基于协作认知负荷理论确立在线协作学习构成框架,从学习者协作状态与任务活动两方面,对在线协作学习关键要素进行量化表征;然后构建了在线协作演化网络,以此进行在线协作学习过程各个状态的演化分析与趋势表征,并提出基于最优控制的协作增效机制;最后通过实际应用与效果分析对上述过程量化方法和增效机制进行验证。实验结果表明,本研究提出的协作增效机制在基本不影响学习者认知负荷的前提下,能够显著提高整体协作学习效果,为认知科学、智能技术与在线协作学习的深度融合发展提供了新思路。


【英文摘要】:

As a social cognitive learning mode in the cross spatial and temporal context, the effective perception of the process state and activity optimization of online collaborative learning are essential for solving the imbalance of cognitive load in group learning and promoting the high-quality co-development. To this end, this paper aims to analyze the state of the online collaborative learning process and guide its appropriateness by integrating collaborative cognitive load theory and intelligent technology to implement and verify the quantitative method and collaborative efficiency mechanism of the online collaborative learning process. This study firstly establishes the framework of online collaborative learning based on collaborative cognitive load theory, and quantitatively characterizes the key elements of online collaborative learning in terms of learners' collaborative states and task activities. Then, an online collaborative evolutionary network is constructed to analyze the evolution of each state and characterize the trend of online collaborative learning process, and a collaborative efficiency mechanism based on optimal control is proposed. Finally, the above process quantification method and efficiency mechanism are verified through practical application and effect analysis. The experimental results show that the collaborative efficiency mechanism proposed in this study can significantly improve the overall collaborative learning effect without affecting the cognitive load of learners, which provides a new idea for the development of the deep integration of cognitive science, intelligent technology and online collaborative learning.

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