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 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.

    • Chen, B., Chang, Y. H., Ouyang, F., & Zhou, W. (2018). Fostering student engagement in online discussion through social learning analytics. The Internet and Higher Education, 37, 21-30.

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

    • The actors are undergrad students in an online course exploring the intersection between technology and ethics in modern societies. They are represented as nodes in the sociogram. Node size is reflective of the student’s degree (sum of in-degree and out-degree). Shading of the nodes is used to distinguish between students who used or did not use the social learning analytics (SLA) toolkit.

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

    • The ties represent the interactions between the students in the online discussion forums. Thickness of the edges reflect the interaction frequency between the two students.

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

    • The interactions are directly relevant to the study’s second research question: “To what extent did the toolkit foster social engagement in online discussions?”

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

    • These actors are a social network as they are part of the same online course and regularly interacting with one another in the discussion forum.

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?

    • Research questions: Are there differences in discourse patterns among students with varying characteristics of participation in the network? How do the discourse patterns and participation characteristics shift over time?

    • Students in the network are invited to attend weekly online meetings, where they interact and collaborate on STEM projects. The relational data collected would be the co-participation of students in the meetings (non-directional).

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

    • The co-participation data would be used to construct a sociogram that provides information on the participation characteristic of students (who who has met with whom and how many times).

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

    • The data for the sociogram would be at the individual level. The data for the analysis of the discourse would be at the utterance level (turn of talk).

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

    • This study seeks to examine both the social and cognitive dimensions of student interaction and collaboration within an informal learning network.

Part II: Data Product

The final step in the workflow/process is sharing the results of your analysis with wider audience. Krumm et al. @krumm2018 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 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 all the code necessary to create your data product.

# YOUR FINAL CODE HERE
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(tidygraph)
## 
## Attaching package: 'tidygraph'
## 
## The following object is masked from 'package:stats':
## 
##     filter
library(ggraph)
library(readxl)

teacher_friends <- read_excel("data/teacher-reported-friends.xlsx", col_names = F)
## New names:
## • `` -> `...1`
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
## • `` -> `...20`
## • `` -> `...21`
## • `` -> `...22`
## • `` -> `...23`
## • `` -> `...24`
## • `` -> `...25`
## • `` -> `...26`
## • `` -> `...27`
student_attributes <- read_excel("data/student-attributes.xlsx")

rownames(teacher_friends) <- 1:27
## Warning: Setting row names on a tibble is deprecated.
colnames(teacher_friends) <- 1:27

teacher_network <- as_tbl_graph(teacher_friends, directed = T)

teacher_edges <- teacher_network |>
  activate(edges) |>
  as_tibble()

teacher_network <- tbl_graph(edges = teacher_edges, 
                             nodes = student_attributes, 
                             directed = T)

ggraph(teacher_network, layout = "stress") + 
  geom_edge_link(arrow = arrow(length = unit(1, 'mm')), 
                 end_cap = circle(1, 'mm'),
                 start_cap = circle(1, 'mm'),
                 alpha = .2) +
  geom_node_point(aes(size = local_size(),
                      color = gender)) +
  geom_node_text(aes(label = id),
                 alpha = .7,
                 repel=F) +
  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.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Narrative

  • This sociaogram displays the friendships among students in the class as perceived by the teacher. The teacher perceives that 8 students (5 female, 3 male) in the class do not have any friends. Of the 19 remaining students, the teacher believes that three friend groups exist in the class. Two students, one female (#16) and one male (#23), are thought to serve as the bridge that connect these groups.

To receive credit for this assignment and earn your first SNA LASER Badge, you will first need to knit and publish this page to Posit Cloud. Next, share the link to published webpage under one of the Badge Artifact columns on the 2023 LASER Scholar Information and Documents spreadsheet: https://go.ncsu.edu/laser-sheet. Once your instructor has checked your link, you will be provided a physical version of the badge pictured at the top of this document!