Purpose

The popularity of MOOCs (massive open online courses) has been increasing rapidly around the world, but there is a lack of research on knowledge diffusion in the MOOC learning forum. This article uses ERGMs (exponential random graph models) to synthesize user attributes from the perspective of social networks, social support and social capital to systematically explore the mechanism of knowledge diffusion in the MOOC learning forum.

Design/methodology/approach

First, the literature related to knowledge diffusion is reviewed to define two forms of knowledge diffusion in the MOOC learning forum, i.e., knowledge transferring and knowledge sharing. Second, based on social network theory, research hypotheses related to centrality, reciprocity, and transitivity are proposed; based on social support theory, research hypotheses related to emotional support, information support and sense of belonging support are proposed; and based on social capital theory, research hypotheses related to the number of posts and number of being liked are proposed. Third, “machine learning”, the most popular course on the Coursera platform, is selected as the research object. The data from March 2016 to December 2019 is obtained, and then, ERGMs are used to simulate the network and test the research hypotheses. Finally, based on the conclusions of this study, corresponding suggestions are proposed from the perspectives of MOOCs platform providers, MOOCs providers and MOOCs learners, to promote the development of MOOCs.

Findings

– (1) In the MOOC learning forum, users with high degree centrality contribute more, actively answering questions from other users, and transferring knowledge to others, but knowledge sharing between two users with high degree centrality is unlikely to occur. (2) Significant reciprocity features exist in knowledge transfer in the MOOC learning forum. (3) There is no significant triangular relationship structure in the knowledge sharing network of the MOOC learning forum, which means that multiple dyad sharing partners are more likely to be formed in the MOOC learning forum. (4) Users with emotion tendency tend to transfer knowledge to other users, and knowledge sharing is likely to occur between individuals with emotion tendency. (5) There is no tendency that users with low learning progress receive knowledge transferred by other users. (6) Teaching assistants, as users with higher knowledge potential in the MOOC learning forum, are prone to transfer knowledge to other users. (7) The trend of knowledge sharing between users in the same region is not obvious. (8) Users with more posts and users with more being liked are more likely to receive knowledge transfer by the other users, and thus, they are easier to obtain answers from. A possible explanation for this result could be that users with more posts and users with more being liked have a higher contribution to the MOOC learning forum, and therefore, questions they raised are more likely to attract attention.

Theoretical implications

First, current research on the MOOC learning forum focuses on the discussion of user behavior, but there is a lack of research on knowledge diffusion. Therefore, in terms of the research perspective, from the aspect of knowledge diffusion, this article defines two forms of knowledge diffusion: knowledge transfer and knowledge sharing. Then, knowledge transfer network and knowledge sharing network are constructed to explore user posting and replying behaviors, to expand the user behavior research in the MOOC learning forum. Second, current research on the user interaction network in the MOOC learning forum lacks comprehensive research on the network structure and user attributes. The micro logic embedded in the network structure must be systematically analyzed. Therefore, the network structure and user attributes are synthesized to explore the influencing factor of knowledge diffusion, which enriches the research content of the MOOC learning forum. Third, ERGMs are suitable for exploring potential factors of network formation, as well as the impact of multiple features simultaneously on network formation. Currently, ERGMs are widely used to research citation network formation mechanisms and social media network formation mechanisms. However, there is still a lack of research on user interaction networks in the MOOC learning forum while applying ERGMs. Therefore, this article introduces ERGMs into the knowledge diffusion research in the MOOC learning forum to provide effective research methods for modeling and simulating the knowledge diffusion networks.

Practical implications

(1) Since high degree centrality users actively answer questions from others, MOOCs platform providers can encourage high degree centrality users to participate in in-depth discussion in forums. Based on the reciprocity of knowledge transfer, MOOCs platform providers can add an information reminder function to remind users to receive information in real time, to promote knowledge transfer between users. Because of the unstable knowledge sharing relationship among learners in the knowledge sharing network of the MOOC learning forum, it is not easy to form a triangular relationship structure. MOOCs platform providers can add a function such as friend circles to enhance the stability of the knowledge sharing relationship and improve the efficiency of the knowledge exchange. (2) Since teaching assistants actively answer questions from other users and play a prominent role in knowledge transfer, course providers can set appropriate incentives for teaching assistants. Because users with emotional tendency are more likely to transfer knowledge, and knowledge sharing is likely to occur between users with emotion tendency, course providers can properly guide users to engage in emotion communication through the participation of teaching assistants, to promote knowledge sharing among users. Because users with low learning progress are usually in a low potential knowledge position, course providers should pay attention to such users to improve their learning interests with help from users in a high potential knowledge position, such as teaching assistants or users who have high learning progress. (3) Because questions raised by users who have more being liked and more posts are more likely to be answered as a result of accumulating social capital, MOOCs learners should actively participate in the course learning forum and have effective interactions with other users. With the increasing number of being liked and the increasing number of posts, users gradually accumulate more social capital to build a foundation for improving their own learning effect.

Originality/value

– First, at present, there are few studies on the mechanism of knowledge diffusion in the MOOC learning forum, and knowledge diffusion research is conducive to exploring the influencing factors of users' learning activities in the course learning forum. Therefore, this paper studies the influence mechanism of knowledge diffusion in the MOOC learning forum by integrating the network structure and user attributes to expand the knowledge diffusion research on the MOOC learning forum. Second, although SNA has been widely used in knowledge diffusion research, it is difficult for SNA to explore the potential factors for the formation of knowledge diffusion networks. ERGMs are based on relational data and use network local features as statistical items to explore the overall structural characteristics of the network. Therefore, this paper simulates knowledge diffusion networks of the MOOC learning forum based on ERGMs, systematically exploring the influence of the network structure and node attributes and their interaction with knowledge diffusion, which helps to reveal the socialization process and internal mechanism of the knowledge diffusion network.

中文翻译：

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基于ERGMs的MOOC学习论坛知识扩散机制研究

目的

MOOCs（大规模开放在线课程）在全球范围内的流行度一直在快速增长，但在MOOC学习论坛中缺乏对知识传播的研究。本文使用ERGMs（指数随机图模型）从社交网络、社会支持和社会资本的角度综合用户属性，系统探索MOOC学习论坛中知识扩散的机制。

设计/方法/方法

首先，回顾与知识传播相关的文献，定义MOOC学习论坛中知识传播的两种形式，即知识转移和知识共享。其次，基于社会网络理论，提出与中心性、互惠性和传递性相关的研究假设；基于社会支持理论，提出情感支持、信息支持和归属感支持的研究假设；并基于社会资本理论，提出与帖子数和被点赞数相关的研究假设。第三，选择Coursera平台上最受欢迎的课程“机器学习”作为研究对象。获取2016年3月至2019年12月的数据，然后使用ERGMs对网络进行模拟并检验研究假设。最后，

发现

– (1) 在MOOC学习论坛中，高度集中的用户贡献更多，积极回答其他用户的问题，向他人转移知识，但不太可能发生两个高度集中的用户之间的知识共享。(2) MOOC学习论坛的知识转移存在显着的互惠特征。(3) MOOC学习论坛的知识共享网络中不存在显着的三角关系结构，这意味着MOOC学习论坛更有可能形成多个二元共享伙伴。(4) 有情感倾向的用户倾向于将知识转移给其他用户，有情感倾向的个体之间很可能发生知识共享。(5) 学习进度低的用户没有接受其他用户传授的知识的趋势。(6) 助教作为MOOC学习论坛中具有较高知识潜力的用户，很容易将知识转移给其他用户。(7) 同一地区用户之间的知识共享趋势不明显。(8) 发帖多的用户和点赞多的用户更有可能接受其他用户的知识转移，从而更容易得到答案。对这一结果的一个可能解释是，帖子越多的用户和被点赞越多的用户对 MOOC 学习论坛的贡献越大，因此他们提出的问题更容易引起关注。(8) 发帖多的用户和点赞多的用户更有可能接受其他用户的知识转移，从而更容易得到答案。对这一结果的一个可能解释是，帖子越多的用户和被点赞越多的用户对 MOOC 学习论坛的贡献越大，因此他们提出的问题更容易引起关注。(8) 发帖多的用户和点赞多的用户更有可能接受其他用户的知识转移，从而更容易得到答案。对这一结果的一个可能解释是，帖子越多的用户和被点赞越多的用户对 MOOC 学习论坛的贡献越大，因此他们提出的问题更容易引起关注。

理论意义

首先，目前MOOC学习论坛的研究主要集中在用户行为的讨论上，而缺乏对知识扩散的研究。因此，在研究视角上，本文从知识扩散的角度定义了知识扩散的两种形式：知识转移和知识共享。然后，构建知识转移网络和知识共享网络，探索用户发帖和回复行为，拓展MOOC学习论坛中的用户行为研究。其次，目前MOOC学习论坛对用户交互网络的研究缺乏对网络结构和用户属性的全面研究。必须系统地分析嵌入在网络结构中的微观逻辑。所以，综合网络结构和用户属性，探索知识扩散的影响因素，丰富了MOOC学习论坛的研究内容。第三，ERGMs 适用于探索网络形成的潜在因素，以及多个特征同时对网络形成的影响。目前，ERGMs被广泛用于研究引文网络形成机制和社交媒体网络形成机制。然而，在应用ERGMs的同时，MOOC学习论坛中仍然缺乏对用户交互网络的研究。因此，本文将ERGMs引入到MOOC学习论坛的知识扩散研究中，为知识扩散网络的建模和模拟提供有效的研究方法。丰富了MOOC学习论坛的研究内容。第三，ERGMs 适用于探索网络形成的潜在因素，以及多个特征同时对网络形成的影响。目前，ERGMs被广泛用于研究引文网络形成机制和社交媒体网络形成机制。然而，在应用ERGMs的同时，MOOC学习论坛中仍然缺乏对用户交互网络的研究。因此，本文将ERGMs引入到MOOC学习论坛的知识扩散研究中，为知识扩散网络的建模和模拟提供有效的研究方法。丰富了MOOC学习论坛的研究内容。第三，ERGMs 适用于探索网络形成的潜在因素，以及多个特征同时对网络形成的影响。目前，ERGMs被广泛用于研究引文网络形成机制和社交媒体网络形成机制。然而，在应用ERGMs的同时，MOOC学习论坛中仍然缺乏对用户交互网络的研究。因此，本文将ERGMs引入到MOOC学习论坛的知识扩散研究中，为知识扩散网络的建模和模拟提供有效的研究方法。以及多个特征同时对网络形成的影响。目前，ERGMs被广泛用于研究引文网络形成机制和社交媒体网络形成机制。然而，在应用ERGMs的同时，MOOC学习论坛中仍然缺乏对用户交互网络的研究。因此，本文将ERGMs引入到MOOC学习论坛的知识扩散研究中，为知识扩散网络的建模和模拟提供有效的研究方法。以及多个特征同时对网络形成的影响。目前，ERGMs被广泛用于研究引文网络形成机制和社交媒体网络形成机制。然而，在应用ERGMs的同时，MOOC学习论坛中仍然缺乏对用户交互网络的研究。因此，本文将ERGMs引入到MOOC学习论坛的知识扩散研究中，为知识扩散网络的建模和模拟提供有效的研究方法。

实际影响

（1）由于高度中心性用户积极回答他人的问题，MOOCs平台提供者可以鼓励高度中心性用户参与论坛的深度讨论。基于知识转移的互惠性，MOOCs平台提供者可以增加信息提醒功能，提醒用户实时接收信息，促进用户之间的知识转移。由于MOOC学习论坛的知识共享网络中学习者之间的知识共享关系不稳定，不易形成三角关系结构。MOOCs平台提供者可以增加朋友圈等功能，增强知识共享关系的稳定性，提高知识交流的效率。(2) 由于助教积极回答其他用户的问题，在知识转移方面发挥突出作用，课程提供者可以为助教设置适当的激励措施。由于有情感倾向的用户更容易传递知识，有情感倾向的用户之间容易发生知识共享，课程提供者可以通过助教的参与，适当引导用户进行情感交流，促进用户之间的知识共享。由于学习进度低的用户通常处于低潜力的知识位置，因此课程提供者应关注此类用户，在高潜力知识位置的用户（例如助教或学习进度高的用户）的帮助下提高他们的学习兴趣. (3) 由于用户提出的问题越多被点赞和发帖越有可能成为社会资本积累的结果，MOOC学习者应积极参与课程学习论坛，与其他用户进行有效互动。随着点赞数的增加和帖子的增加，用户逐渐积累了更多的社会资本，为提升自己的学习效果奠定了基础。

原创性/价值

——首先，目前关于MOOC学习论坛知识扩散机制的研究较少，知识扩散研究有利于探索课程学习论坛用户学习活动的影响因素。因此，本文通过整合网络结构和用户属性来研究MOOC学习论坛中知识扩散的影响机制，以拓展MOOC学习论坛上的知识扩散研究。其次，虽然SNA在知识传播研究中得到了广泛的应用，但SNA很难探索形成知识传播网络的潜在因素。ERGM 以关系数据为基础，以网络局部特征为统计项，探索网络的整体结构特征。所以，