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: 

Part I: Reflect and Plan

Use the institutional library (e.g. NCSU Library), Google Scholar or search engine 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.

de Laat, M., Lally, V., Lipponen, L. et al. Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. Computer Supported Learning 2, 87–103 (2007). https://doi.org/10.1007/s11412-007-9006-4

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

    • Students in an online course and one tutor, sociogram (circles with letters)

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

    • Behaviorial interaction, sociogram (lines with arrows)

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

    • Dynamics of changes in members’ behavior during collaborative project over time

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

    • Collaborative project

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:

How are interaction patterns different between high and low interaction students in case-based discussions?

  1. What relational data would need to be collected?

    • Students posts in online discussions

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

    • To get insight into how low interaction students behave in social network.

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

    • Centrality and density will be analyzed for high and low interactions; edge list will be created with edge attributes to understand low interaction students

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

    • this will show low interaction students positions within the socila network

Part II: Data Product

Using one of the data sets provided in your data folder, your goal for this lab is to create a polished sociogram that visually represents this network. For example, you may be interested in examining how shared characteristics among school leaders might help explain tie formation, such as gender, level of trust in colleagues, or whether they work at the school or district level.

Alternatively, you may use your own data set to estimate models akin to those we estimated in the guided practice. 

I highly recommend creating a new R script in your lab-1 folder to complete this task. When your code is ready to share, use the code chunk below to share the final code for your model and answer the questions that follow.

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0      ✔ purrr   0.3.4 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.2      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(tidygraph)
## 
## Attaching package: 'tidygraph'
## 
## The following object is masked from 'package:stats':
## 
##     filter
library(ggraph)
library(igraph)
## 
## Attaching package: 'igraph'
## 
## The following object is masked from 'package:tidygraph':
## 
##     groups
## 
## The following objects are masked from 'package:dplyr':
## 
##     as_data_frame, groups, union
## 
## The following objects are masked from 'package:purrr':
## 
##     compose, simplify
## 
## The following object is masked from 'package:tidyr':
## 
##     crossing
## 
## The following object is masked from 'package:tibble':
## 
##     as_data_frame
## 
## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum
## 
## The following object is masked from 'package:base':
## 
##     union
library(readxl)
interaction_edges <- read_excel("data/interaction_edges.xlsx") 
pim_nodes <- read_excel("data/pim_nodes.xlsx")
interaction_network <- tbl_graph(edges = interaction_edges,
                          nodes = pim_nodes, 
                          directed = TRUE)
autograph (interaction_network)

autograph(interaction_network,
          node_label = id,
          node_colour = interaction)

plot(interaction_network)

ggraph(interaction_network, layout = "fr") + 
  geom_edge_link(arrow = arrow(length = unit(1, 'mm')), 
                 end_cap = circle(3, 'mm'),
                 start_cap = circle(3, 'mm'),
                 alpha = .1) +
  geom_node_point(aes(size = local_size())) +
  geom_node_text(aes(label = id,
                      size = local_size()),
                 repel=TRUE) +
  theme_graph()
## Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.

Knit & Submit

Congratulations, you’ve completed your Intro to SNA Badge! Complete the following steps to submit your work for review:

  1. Change the name of the author: in the YAML header at the very top of this document to your name. As noted in Reproducible Research in R, The YAML header controls the style and feel for knitted document but doesn’t actually display in the final output.

  2. Click the yarn icon above to “knit” your data product to a HTML file that will be saved in your R Project folder.

  3. Commit your changes in GitHub Desktop and push them to your online GitHub repository.

  4. Publish your HTML page the web using one of the following publishing methods:

    • Publish on RPubs by clicking the “Publish” button located in the Viewer Pane when you knit your document. Note, you will need to quickly create a RPubs account.

    • Publishing on GitHub using either GitHub Pages or the HTML previewer.

  5. Post a new discussion on GitHub to our SNA Badges forum. In your post, include a link to your published web page and a short reflection highlighting one thing you learned from this lab and one thing you’d like to explore further.