Social Network Analysis

Although social network analysis (SNA) and its educational antecedents date back to the early 1900s, the popularity of social networking sites like Twitter and Facebook have raised awareness of, and renewed interests in, social networks and their influence. As the use of digital resources continues to expand in education, data collected by these educational technologies has also greatly facilitated the application of network analysis to teaching and learning. The SNA modules are designed to prepare STEM education researchers to apply network analysis in order to better understand and improve student learning and the contexts in which learning occurs. The presentations, readings, and case studies for each module draw from the excellent book, Social Network Analysis and Education (Carolan 2014). Collectively, the modules provide education researchers with an overview of social network theory, examples of network analysis in STEM educational contexts, and applied experience with widely adopted tools and techniques.

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 Repository for Instructors
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Module 1: Network Analysis for Newbies

The first module is a gentle introduction to data collection, management and visualization. The focus of our Essential Readings and case study in this lab is to help LASER Scholars gain a general understanding of key SNA concepts and terminology, as well as develop a basic comfort level with representing networks visually. Our SNA Case Study: Who’s Friends with Who in Middle School is guided by the work of Pittinsky and Carolan Pittinsky and Carolan (2008) and compares teacher perceptions and student reports of classroom middle school friendship and helps reinforce the importance of treating behavioral and cognitive classroom friendship networks and ties as distinct. Finally, the Intro to SNA Badge provides an opportunity create your own data product and to reflect on how theses concepts and techniques might apply to your own research.

Conceptual
Overview		 An Introduction to Social Network Analysis
Code Along Data Structures & Sociograms in R | Python
Readings &
Reflection		 Background and Basic Concepts
Case Study Who’s Friends with Who in Middle School | R Key | Python Key
Badge Intro to SNA
Module Survey Feedback Form After Finishing Module

Module 2: Network Management & Measurement

Module 2 moves beyond basic concepts of network analysis and takes a closer look at the collection, management, and measurement of network data. Our Essential Readings examine the different levels at which social networks can be analyzed, as well as common network measures for describing properties of complete networks. Our Case Study: A Tale of Two MOOCs is based on a study by Kellogg, Booth, and Oliver (2014) and compares discussion networks from two courses using an open educational dataset prepared by Kellogg and Edelmann (2015) as part of the Friday Institute’s work around Massively Open Online Courses for Educators (MOOC-Eds). Finally, the Measurement Badge provides an opportunity create your own data product and to reflect on how theses concepts and techniques might apply to your own research.

Conceptual
Overview		 Data Collection & Quality
Code Along Density, Reciprocity, & Centrality with R | Python
Readings &
Reflection		 Data Management & Network Measurement
Case Study A Tale of Two MOOCs | R Key | Python Key
Badge Network Measurement Badge
Module Survey Feedback Form After Finishing Module

Module 3: Groups, Positions, & Egocentric Analysis

Module 3 shifts the focus from complete network analysis and zooms in on methods and measures for analyzing groups, positions, and individual actors. Our Essential Readings and case study explore both “top-down” and “bottom-up” approaches to identify a network’s groups and extend measures introduced in the previous lab to identify individuals central to the network. Our SNA Case Study: Hashtag Common Core is inspired by the work of Supovitz et al. (2017) who examined groups and key actors that emerged during the intense Twitter debate surrounding the Common Core State Standards. You can learn more about their work on the expansive and interactive website for the #COMMONCORE Project. Finally, the Groups & Egos Badge provides an opportunity create your own data product and to reflect on how theses concepts and techniques might apply to your own research.

Conceptual
Overview		 Group Identification in Networks
Code Along Components, Cliques & Key Actors with R | Python
Readings &
Discussion		 Groups, Positions, and Egocentric Analysis
Case Study Hashtag Common Core | R Key | Python Key
Badge Groups & Egos
Module Survey Feedback Form After Finishing Module

Module 4: Statistical Inference & Network Models

Module 4 wraps up our work with SNA and examines recent advances in inferential statistics that can be used to make predictions from social network data and test hypotheses we have about a network of interest. Through our Essential Readings, we’ll learn about different techniques that make use of simulations to model network data and how these statistical models are used to address questions that more completely reflect the complexity of educational settings. For example, our SNA Case Study: Birds of a Feather Lead Together is inspired by the work of Daly and Finnigan (2011) makes use of Exponential Random Graph Models (ERGMs) to examine social processes (e.g. reciprocity and homophily) that might explain how school and district-level leaders select peers for collaboration or confidential exchanges. Finally, the Models & Inference Badge provides an opportunity create your own data product and to reflect on how theses concepts and techniques might apply to your own research.

Conceptual
Overview		 Network Inference & Applications
Code Along Intro to ERGMs with R | Python
Readings &
Discussion		 Network Modeling & Inference
Case Study Birds of a Feather Lead Together | R Key | Python Key
Badge Models & Inference
Module Survey Feedback Form After Finishing Module

Microcredential

The culminating activity for the SNA Modules is designed to provide you some space for independent analysis of a self-identified data source. To earn your SNA Microcredential, you are required to demonstrate your ability to formulate a basic research question appropriate to a social network context, wrangle and analyze relational data, and communicate key findings. Your primary goal for this analysis is to create a simple data product that illustrates key findings by applying the knowledge and skills acquired from the essential readings and case studies.

Microcredential Social Network Analysis and Education

Essential Readings

Carolan, Brian. 2014. “Social Network Analysis and Education: Theory, Methods & Applications.” https://doi.org/10.4135/9781452270104.
Daly, Alan J, and Kara S Finnigan. 2011. “The Ebb and Flow of Social Network Ties Between District Leaders Under High-Stakes Accountability.” American Educational Research Journal 48 (1): 39–79. https://www.jstor.org/stable/27975281.
Kellogg, Shaun, Sherry Booth, and Kevin Oliver. 2014. “A Social Network Perspective on Peer Supported Learning in MOOCs for Educators.” International Review of Research in Open and Distributed Learning 15 (5): 263–89. https://www.erudit.org/en/journals/irrodl/2014-v15-n5-irrodl04945/1065545ar.pdf.
Kellogg, Shaun, and Achim Edelmann. 2015. “Massively Open Online Course for Educators (MOOC-Ed) Network Dataset.” British Journal of Educational Technology 46 (5): 977–83. https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.12312.
Pittinsky, Matthew, and Brian V Carolan. 2008. “Behavioral Versus Cognitive Classroom Friendship Networks: Do Teacher Perceptions Agree with Student Reports?” Social Psychology of Education 11: 133–47. https://link.springer.com/content/pdf/10.1007/s11218-007-9046-7.pdf.
Supovitz, Jonathan, Alan J Daly, Miguel del Fresno, and Christian Kolouch. 2017. “Commoncore Project.” Retrieved April 3: 2019. https://www.hashtagcommoncore.com.