Intro to SNA

SNA Module 1: Badge

The final activity for each learning lab provides space to work with data and to reflect on how the concepts and techniques introduced in each lab might apply to your own research.

To earn a badge for each lab, you are required to respond to a set of prompts for two parts: 

• In Part I, you will reflect on your understanding of key concepts addressed in the case study and essential readings and begin to think about potential next steps for your own study.

• In Part II, you will create a simple data product in R that demonstrates your ability to apply a data analysis technique introduced in this learning lab.

Part I: Reflect and Plan

Use your institutional library, Google Scholar, or search engine of your choice to locate a research article, presentation, or resource that applies social network analysis to an educational context or topic of interest. More specifically, locate a network study that makes use of sociograms to visualize relational data. You are also welcome to select one of the research papers listed in the essential readings that may have piqued your interest.

1. Provide an APA citation for your selected study.

2. Who are the network’s actors and how are they represented visually?

3. What ties connect these actors and how are they represented visually?

4. Why were these relations of interest to the researcher?

5. Finally, what makes this collection of actors a social network?

Draft a research question for a population you may be interested in studying, or that would be of interest to educational researchers, and that would require the collection of relational data and answer the following questions:

1. What relational data would need to be collected?

2. For what reason would relational data need to be collected in order to address this question?

3. Explain the analytical level at which these data would need to be collected and analyzed.

4. How does this differ from the ways in which individual or group behavior is typically conceptualized and modeled in conventional educational research?

Part II: Data Product

The final step in the workflow/process is sharing the results of your analysis with wider audience. Krumm et al. Krumm, Means, and Bienkowski (2018) have outlined the following 3-step process for communicating with education stakeholders findings from an analysis:

1. Select. Communicating what one has learned involves selecting among those analyses that are most important and most useful to an intended audience, as well as selecting a form for displaying that information, such as a graph or table in static or interactive form, i.e. a “data product.”

2. Polish. After creating initial versions of data products, research teams often spend time refining or polishing them, by adding or editing titles, labels, and notations and by working with colors and shapes to highlight key points.

3. Narrate. Writing a narrative to accompany the data products involves, at a minimum, pairing a data product with its related research question, describing how best to interpret the data product, and explaining the ways in which the data product helps answer the research question and might be used to inform new analyses or a “change idea” for improving student learning.

For your first SNA badge, your goal is to distill the analysis from our case study into a simple “data product” designed to illustrate a key finding from our analysis. For the purposes of this task, imagine that your audience consists of teachers and school leaders who have limited background in SNA and adapt the following steps accordingly:

1. Select. Select the teacher and/or student sociogram we created in the case study, or create an entirely new sociogram if so motivated, to share with teacher and school leaders.

2. Polish. Refine this sociogram a little more to help illustrate an interesting aspect about the network of classroom friendships reported by teachers and/or students.

3. Narrate. Write a brief narrative to accompany your visualization and/or table describing how best to interpret the data product and how it might why it might be informative or useful to your intended audience.

I highly recommend creating a new R or Python script in your module-1 folder to complete this task. When your code is ready to share, use the approriate code chunk below to share the all the code necessary to create your data product.

# YOUR FINAL R CODE HERE

# YOUR FINAL Python CODE HERE

Narrative

• WRITE YOUR NARRATIVE OF THE NETWORK HERE

To receive credit for this assignment and earn your first SNA LASER Badge, you will need to render this document and publish via a method designated by your instructor such as: Posit Cloud, RPubs , GitHub Pages, Quarto Pub, or other methods. Once you have shared a link to you published document with your instructor and they have reviewed your work, you will be provided a physical or digital version of the badge pictured at the top of this document!

References

Krumm, Andrew, Barbara Means, and Marie Bienkowski. 2018. Learning Analytics Goes to School. Routledge. https://doi.org/10.4324/9781315650722.