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    学习者同步和异步交互模式的比较与整合研究 ——基于社会网络和多层网络分析的方法

    Research on Comparison and Integration of Synchronous and Asynchronous Interaction Patterns for Learners-Based on Social Network Analysis and Multiplex Network Analysis

【作      者】:

胡天慧, 刘三女牙, 粟 柱, 周子荷, 余建伟, 柴唤友


【关 键 词 】:

在线教育; 在线学习; 学习者交互; 社会网络分析; 多层网络分析


【栏      目】:

网络教育


【中文摘要】:

利用新型分析方法综合分析学习者交互以获取关于其学习过程的有益见解,是在线教育研究和实践的迫切需求。文章以中部某高校的一门科教融合创新在线课程为例,采用社会网络分析和多层网络分析方法,分别对学习者的在线同步和异步交互模式进行比较与整合分析,并探讨其与学习成绩之间的关系。研究结果发现:相比同步交互网络,异步交互网络更为稠密,且其个体指标值相对更高;相比同步和异步两个单层交互网络,基于二者整合而得到的多层交互网络在网络连边数量上显著增多,在个体指标值上显著增高,而且学习者在多层和单层交互网络中的地位相对不同;相比单层交互网络,多层交互网络的部分个体指标与学习成绩的相关性相对更高。研究为分析和理解学习者交互提供了研究方法论和教学实践方面的启示性意义,有助于改进对在线群体学习过程的动态调控与精确引导并优化现有教学模式。


【英文摘要】:

There is an urgent need for online education research and practice to utilize new analytical methods to analyze learner interactions to gain useful insights about how learning occurs. Taking an online course characterized by integrating and innovating science and education from a university in central China as an instance, this paper utilizes social network analysis and multiplex network analysis to respectively compare and integrate learner's interaction patterns in synchronous and asynchronous online discussions, and explore their correlations with learning performance. The results shows that compared to the synchronous interaction network, the asynchronous one is denser, and its individual indices are relatively higher. Besides, compared with the two single-layer networks, i.e., synchronous and asynchronous interaction networks, the multiplex networks integrated by them are significantly higher in the number of network edges and individual indices, and learners have different positions in the multiplex networks and single-layer networks. Finally, the correlations between certain individual indexes and course grade are relatively higher in multiplex networks compared to those in single-layer networks. This study provides methodological and practical implications for the analysis and understanding of learner interaction, which can contribute to improving the dynamic regulation and accurate guidance of online group learning processes and optimizing existing teaching models.

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