Social Network Analysis MOOC

This May Shane and I are teaching a new course on Social Network Analysis on edX.

MODULE 1: Fundamentals of SNA

  • What are the basic terms we use when we do network analysis? How to represent network data for the analysis? How to calculate basic network analysis metrics, such as density, network size, centralisation, degree distribution, clustering, diameter, and degree centralities?

If fully a novice, do the Readings first (Intro to networksAnother intro to networks, An example of the application written as a primer to network analysis ), then read Extended Notes for Module 1.

If you have some notion of the terms and want the know-how, download the Annotated Script and read it (open with any text editor), or upload in R and follow through on your own or using the Tutorials (Part A, Part B, Part C, Part D). R and Gephi are just the tools to count the metrics and visualise networks. You would need an edgelist file and a nodelist file for R analysis.

If an expert or a false beginner, just jump to the Assignments and test your knowledge.

Watch me summarise main key takeaways after you reflect on your learning.

MODULE 2: Designing Network Studies

  • What questions can we ask about the networks? What ethical considerations to have? What concepts help explain social behavior in networks? How to link graph metrics with theoretical constructs? How to interpret the network metrics?

If a full novice, start with Extended Notes Module 2, and then move to Readings (Use of SNA in Education, Ethical Dilemmas in SNA, Example of SNA 1, Example of SNA 2, Example of SNA 3).

Some self-study questions: on checking thinking here, on checking practical skills here, on linking metrics with operationalisations here , on asking network questions here.

Download and read through the Annotated Script (open with any text editor) regardless of whether you want to do the analysis. If you are keen on using the script, use Tutorials (Part A, Part B, Part C) to go through the script. You do not need to download any additional data to conduct the analysis.

Having looked through these, move to the Assignments – they will check your understanding and effort in using conceptual thinking about the networks.

Watch me summarise main key takeaways after you reflect on your learning.

MODULE 3: SNA in Learning Analytics: Digital Networks

  • How does one’s thinking about the network interpretation and operationalisation of network ties, edges and metrics change once we use digitally collected data (not self-reported one)? What still makes sense?  How does learning analytics approach the analysis of networks? What changes? What becomes limited?

If a complete novice, start with Extended Notes Module 3, then cherry pick your case studies that exemplify learning analytics applications of SNA (Case 1, Case 2, Case 3, Case 4).

There are some other readings in the module but the reality is that there are diverse applications of SNA in learning analytics. They are not better or worse, they are just different. My mission was to help you think critically about them to choose what works for you. To challenge yourself, you can use the assignment I designed that is a checklist for network study design. Or just start with your network study and good luck!