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An Approach Based on Social Network Analysis Applied to a Collaborative Learning Experience

机译：基于社交网络分析的协作学习体验方法

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The Social Network Analysis (SNA) techniques allow modelling and analysing the interaction among individuals based on their attributes and relationships. This approach has been used by several researchers in order to measure the social processes in collaborative learning experiences. But oftentimes such measures were calculated at the final state of experiences, what may be hardly representative of students’ behaviours during the learning processes. Therefore, a temporal dimension in SNA metrics may extend and improve the understanding about students’ interactions in a collaborative scenario. In this respect, this paper presents a systematic review about SNA metrics used for analysing CSCL scenarios and proposes to trace the behaviour of such metrics during experiences through the inclusion of a temporal dimension. In order to expose this approach, a real collaborative learning experience, supported by a platform called SMLearning System, was analysed. We found that social relationships among students tend to be symmetric, i.e., there was a proportional distribution of efforts and contributions of students, which is an expected condition in a collaborative scenario. Such observations are based on the temporal behaviour of the reciprocity metric and the correlation between in- and out- degree centrality metrics measured in time.
机译：社交网络分析（SNA）技术允许根据个人的属性和关系来建模和分析个人之间的交互。一些研究人员已使用此方法来衡量协作学习体验中的社会过程。但是，通常，这些量度是根据最终的体验状态来计算的，这几乎不能代表学生在学习过程中的行为。因此，SNA指标中的时间维度可以扩展并改善在协作方案中对学生互动的理解。在这方面，本文对用于分析CSCL场景的SNA指标进行了系统的综述，并提出了通过包含时间维度来跟踪体验期间此类指标的行为。为了展示这种方法，分析了由SMLearning System平台支持的真正的协作学习体验。我们发现学生之间的社会关系趋于对称，即学生的努力和贡献成比例分布，这是在协作场景中的预期条件。此类观察基于互惠度量的时间行为以及及时测量的度数和度数中心度度量之间的相关性。

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