RL2023-4
An Introduction to Social Network Analysis and Education Research: Core Concepts and Applications with R
Thursday, August 10, 2023
 
INSTRUCTORS
Shaun B. Kellogg, North Carolina State University

Bodong Chen, University of Pennsylvania

Oleksandra Poquet, Technical University of Munich

Jeanne M. McClure, North Carolina State University

Although social network analysis (SNA) and its educational antecedents date back to the early 1900s, the popularity of social networking sites have raised awareness of and renewed interests in networks and their influence. Moreover, as the use of digital resources continues to expand in education, data collected by these educational technologies and corresponding advances in computing power has also greatly facilitated the application of network analysis in education research. This course is designed to introduce education researchers with little or no background in SNA to social network theory, examples of network analysis in educational contexts, and applied experience analyzing real-world data sets. To support scholars’ conceptual understanding of SNA as both a theoretical perspective and an analytical method, the instructors will provide short presentations and facilitate peer discussion on topics ranging from broad applications of SNA in educational contexts to specific approaches for data collection and storage. This course will also provide scholars with applied experience analyzing network data through code-alongs and interactive case studies that use widely adopted tools (e.g., R, RStudio, and GitHub) and demonstrate common techniques (e.g, network visualization, measurement, and modeling). Collectively, these activities will help scholars both appreciate and experience how SNA can be used to understand and improve student learning and the contexts in which learning occurs. While prior experience with R, RStudio, and GitHub is recommended to complete more advanced activities, it is not required.