Learning involves relations, interactions and connections between learners, teachers and the world at large. Such interactions are essentially temporal and unfold in time. Yet, researchers have rarely combined the two aspects (the temporal and relational aspects) in an analytics framework. Temporal networks allow modeling of the temporal learning processes i.e., the emergence and flow of activities, communities, and social processes through fine-grained dynamic analysis. This can provide insights into phenomena like knowledge co-construction, information flow, and relationship building. This chapter introduces the basic concepts of temporal networks, their types and techniques. A detailed guide of temporal network analysis is introduced in this chapter, that starts with building the network, visualization, mathematical analysis on the node and graph level. The analysis is performed with a real-world dataset. The discussion chapter offers some extra resources for interested users who want to expand their knowledge of the technique.

翻译：学习涉及学习者、教师及外部世界之间的关系、互动与连接。这类互动本质上具有时间性，并在时间中展开。然而，研究者鲜少将时间维度与关系维度整合至同一分析框架中。时间网络允许对时间性学习过程（例如活动的涌现与流动、社群及社会过程）进行建模，通过细粒度的动态分析揭示知识共建、信息流动及关系构建等现象。本章介绍了时间网络的基本概念、类型与技术，并提供了从网络构建、可视化到节点与图层面数学分析的完整指南。分析基于真实世界数据集展开。讨论章节为希望拓展该技术知识的兴趣读者提供了额外资源。