The rapid development of online learning networks has resulted in the widespread use of recorded educational contents. While the community structure of those networks may have an influence on the use of contents, research on detecting online learning communities and investigating their structures using social network analysis (SNA) methods is scarce. The purpose of the research presented here is to investigate the structure of online learning networks and their users’ engagement patterns. In this study, Khan Academy, a widely used video learning repository, will be used as a case. Community detection algorithms are used to detect the development of online learning communities and network performance and effectiveness measures are applied to assess the network structure, effectiveness, and efficiency of a large dataset consisting of 359,163 users that interacted with Khan Academy's videos with over 3M questions and answers. The results demonstrate that different community detection algorithms can be implemented on learning networks and produce good learning communities which are not necessarily related to a domain or a topic. Measures such as density can be used to measure social presence while centrality measures are used to define central users and hubs in the communities. This study complements previous research that shed the light on the power and potential of SNA measures to structurally evaluate and detect online learning communities.