About this courseSkip About this course
In this course, you will learn how relationships between people, artifacts, and ideas within learning settings can be analyzed and interpreted through social network analysis (SNA). You will learn how to prepare data and map these relationships to help you understand how people communicate and exchange information.
The course will review foundational concepts and applications of social network analysis in learning analytics. You will also learn how to use igraph and statnet R packages to manipulate, analyze, and visualize network data.
What you'll learnSkip What you'll learn
You will learn to:
- apply the basics of social network analysis (SNA) at the network level (density, clustering, degree distribution, etc.); at the node level (degree, betweenness, closeness); at the sub-graph level (triads, communities);
- design a research study using relational data;
- conduct SNA of data collected in a learning setting;
- apply basic functions of igraph and statnet R packages to analyze data.
Week 1: Navigating the Language of Networks
Introduction to networks including the basic concepts in social network analysis, i.e. nodes, edges, adjacency matrix, one and two-mode networks, node degree, connected components, average shortest path, diameter, preferential attachment, network centrality. The week will involve a hands-on task showing students how to calculate basic metrics in R.
Week 2: Applying Network Analysis in Educational Research
Overview of educational research and evidence produced using SNA applications, including differentiation between self-reported and digitally collected network data; ethical considerations; interpretation of basic metrics. The week’s task will include exploratory analysis of the selected dataset, and interpretation of results.
Week 3: The Use of Network Analytic Techniques in Learning Analytics
Introduction to the analysis of socio-technical networks, and applications of network analytic techniques in LA, i.e. community detection, bipartite network analysis, network clustering, integration with text analysis. Presentation of community detection, information flow analysis, and statistical approaches in network analysis. The students will be expected to select one approach out of those presented, and implement it on one of the suggested datasets in R.
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