基于多层网络建模的在线学习社区群体动力激活研究  

Research on Group Dynamics Activation of Online Learning Community Based on Multiplex Network Modeling

作　　者：王辞晓[1] 李林泽 WANG Cixiao;LI Linze(Beijing Normal University,Beijing 100875)

机构地区：[1]北京师范大学,北京100875

出　　处：《现代远距离教育》2024年第1期69-78,共10页Modern Distance Education

基　　金：国家自然科学基金2022年度青年科学基金项目“基于社会-行为-认知-情感的在线协作角色互动分析与干预研究”(编号:62207003);北京师范大学教育学部2022年度惠妍国际学院国际教育研究课题“高等教育在线国际化背景下的课程质量评估与提升路径研究”(编号:2023HICR0004)。

摘　　要：在线学习社区的群体动力激活有助于促进多元复杂背景的学习者进行深度联通与知识创新。通过挖掘与整合学习者、概念、话题在复杂网络中的量化指标来构建群体动力激活机制,进而为在线学习社区群体互动与持续发展提供思路建议。基于多层网络分析方法中的多路复用网络和多维型多层网络,构建了社会交互、认知共现、概念关联和话题关联四层关系网络的多层网络模型,并设计了包含学习同伴、学习资源、学习话题推荐策略的群体动力激活机制。以联通主义课程cMOOC 5.0为应用情境,验证了该多层网络模型应用于在线学习社区的有效性,并根据建模结果提出具体的群体动力激活机制:基于社会交互与认知共现网络推荐学习同伴以强化群体凝聚力;基于概念关联网络推荐学习资源以提升群体驱动力;基于概念关联和话题关联网络推荐学习话题以降低群体耗散力。继而将多层网络建模的研究价值从微观层面的学习规律挖掘扩展至中观层面的教学干预设计。The group dynamics activation of online learning communities can help promote deep connectivity and knowledge innovation among learners from diverse and complex backgrounds.By mining and integrating the quantitative indicators of learners,concepts and topics in complex networks,the group dynamics activation mechanism is constructed,and then the ideas and suggestions for the group interaction and sustainable development of online learning communities are provided.Based on the multiplexed network and multi-dimensional multiplex network analysis method,a four-layer relationship network model of social interaction,cognitive co-occurrence,concept association and topic association was constructed,and a group dynamic activation mechanism including learning peers,learning resources and learning topic recommendation strategies was designed.Taking a connectivism online course named cMOOC 5.0 as the application scenario,the effectiveness of the multi-layer network model applied to the online learning community was verified,and the specific activation mechanism of group dynamics was proposed according to the modeling results:the network recommends learning peers based on social interaction and cognitive co-occurrence to strengthen group cohesion,recommends learning resources based on concept association network to improve group driving force,and recommends learning topics based on concept association and topic association network to reduce group dissipation.The research value of multi-layer network modeling is extended from the learning law mining at the micro level to the teaching intervention design at the meso-level.

关 键 词：多层网络分析 联通主义 群体动力理论 社群学习 推荐机制 

分 类 号：G43[文化科学—教育学]