Welcome to the 2017 LASI Social Network Analysis Workshop!
Title: SNA for Learning Analytics in Formal and Informal Learning Environments.
General Description : Course management systems, games for learning, synchronous collaboration systems and other technologies each have different electronic trace data technical characteristics. Social Network Analysis (SNA) is a research method applied to the analysis electronic trace data (log files) from these environments. Typically, SNA for Learning Analytics is used in concert with complementary research methods, including computational linguistics, ethnography and ethnomethodologically informed analysis of participant behavior.
Participants will work through examples from learning management systems, collaborative software construction and games for learning environments. Participants are also encouraged to submit their own data for pre-workshop assessment and discussion.
The SNA approach to Learning Analytics focuses on the Group Informatics Model and its related, two-phase methodological approach in detail. Group Informatics is the workshop organizer’s systematic methodology and ontology for scaffolding researcher decision making. Phase one of the methodological approach centers on a set of guiding research questions aimed at directing the application of Group Informatics to new corpora of integrated electronic trace data and qualitative research data. Phase two of the methodological approach is a systematic set of steps for transforming electronic trace data into weighted social networks. We show that the Group Informatics methodological approach is the starting point for important discussions aimed at advancing empirically and theoretically informed analysis of electronic trace data focused on small groups. Group Informatics can also be used as a foundation for pursuing research questions in a range of technology mediated environments where formal and informal learning take place. Participants will learn how to perform SNA technologically; but the frame of that technology use for research is much broader.
- A nominal list of 4-9 readings. At least four will be required, but they are conceptual in nature.
- Some familiarity with R or Python programming will be helpful, but is not required.
Details on pre-workshop Preperation
Prior to the workshop, I ask you to read seven papers of mine, a light read of a book and a paper by Granovetter, which is a classic in the field. Together, these readings provide a conceptual progression of how I apply network analysis to evaluate learning and performance in a range of online contexts that support formal and informal learning. The list of readings is below. I recommend reading them in order. If you need to prioritize, I have bolded the ones that are most essential.
- This book by Linton Freeman provides an excellent history of the multidisciplinary evolution of Social Network Analysis
- Group Informatics: A Short History
- Group Informatics: Measuring Influence in Context
- Performance and Social Structure in Learning Groups:
- Group Informatics: Explained
- Group Informatics for user modeling and personalization:
- Network Analysis of Trace Data to Support Group Work:
- Open Collaboration Data Exchange: Advancing a Technical System
- Granovetter: Strength of Weak Ties
All activities and examples are in R. You should load, if you can, “R” (r-project.org), “R Studio” (rstudio.com) and the GitHub client on your computer ahead of time. I will give access to the private GitHub repo to participants. It is the subject of a book I am writing, so its not open source yet.
A Good Overview of the History of Social Network Analysis
I think you may find it surprising how such a prominent technique for understanding social structure evolved from a number of distinct disciplines. Most recently, SNA as a method has taken what I think of as a signifiant mathematical turn (I make no value judgments about this … ok, I kind of do). Using Social Network Analysis to identify structure in technology mediated learning environments could be as “easy” as treating each user act as an edge, and each user as a node. (Node == “Thing/Person”; Edge == “Connection between two things or people.) In the first four readings (or “airplane scanning tasks) I sent you in advance of our workshop, you note that I am making an argument for a systematic approach to using SNA in learning analytics. An approach that is both empirically and theoretically informed, and guides the researcher to make explicit decisions about how nodes are defined and how the strength of connection between those nodes (edges or “lines”) are presented.
Outline of our Journey Through Social Network Analysis as a Learning Analytics Method
Social Network Analysis is widely used, and sometimes not used well to understand technology mediated learning and a host of other phenomena that emerge online. Howison, Wiggins and Crowston point out a number of significant flaws in prior applications of SNA methods, developed for the study of human’s and the social structures they create in the physical world, to “electronic trace data” (log files) generated through online behavior. The Freeman history, the Howison, Wiggins and Crowston critique and the four papers I provided help frame the challenges of using SNA well as a method for making sense of learning and developing learning analytics.
There will be code, but you do not need to be an experienced developer to gain knowledge from this workshop. Everything we do will connect concepts to worked examples. The concepts will fuel research questions, and the worked examples will provide a foundation for setting about to answer them.