Focused Field School 2023:
Network Analysis Techniques
Dates: June 26-30, 2023
Location: Rochester Institute of Technology
Applications due on April 15, 2023
Applicants will be notified by May 1, 2023
This 5-day, intensive, in-person PEER focused field school gives opportunities for previous PEER participants to expand their methodological expertise in educational research practices by focusing on network analysis techniques to analyze relational data in educational settings. If you are interested in this focused field school and have not yet attended an introductory PEER field school, consider also applying for PEER-Rochester 2023 which immediately precedes the focused field school (you can apply for both field schools with the link above).
Costs at registration are $100, which includes breakfast, lunch and materials. We have negotiated a reduced housing cost of $400 for the week for those attending from outside Rochester. Participants are responsible for travel to and from Rochester and evening meals.
A limited number of scholarships are available which can be used to defray a portion of registration and housing costs.
All times are in Eastern Time (ET)
- Virtual Kickoff: Thursday, June 15, 2:30-4:00pm on zoom
- Monday-Friday, June 26-30
- MTHF: 9:00am-4:00pm
- W: 9:00am-12:00pm
Attendance is required for all sessions.
Who Should attend?
PEER targets a broad diversity of experience and interest in DBER. The workshop is appropriate for:
- Faculty not currently engaged in DBER but interested in learning about theories and methodologies for possible future research
- Current DBER researchers looking to build or broaden their network of collaborators and engage in generative discussions about existing and new projects
- Graduate students or postdoctoral researchers who want to learn more about DBER project management and building a successful research program
- Faculty at teaching-focused universities interested in using research methodology to improve or assess their teaching and/or publish in Scholarship of Teaching & Learning
As a focused field school, we have two requirements for participation:
- Participation in at least one introductory PEER field school (such as PEER-Rochester 2023)
- Access to an appropriate data set that can be analyzed during the field school (template data will be provided for some tasks). See below for more information on network analysis techniques and appropriate data.
What do we cover?
Participants will be introduced to the underlying theory of network analysis and how to utilize social network analysis to analyze relational data in educational settings. The development arc traverses all stages of this quantitative methodology, including cleaning and reshaping data, addressing missing data, developing intuition for inference, and data visualization in R. Participants will be able to:
- import data into R and manage data files;
- clean data and combine data sets;
- investigate the potential impacts of missing data;
- visualize data to explore the data and generate publication quality figures; and
- analyze networked data at multiple levels (node-level, graph-level, and model-level).
Networked data can take many forms, and many forms of data can be treated as networks.
- The most common form of network data is adjacency data, which is a set of nodes and the connections between these nodes (stored most often as a square matrix or as an edgelist). In educational settings this might include a group of students and the connections could be homework partners.
- Co-occurrence data is fairly typical, this is like a situation where Persons A, B, and D all attended a meeting together, so by virtue of the probability that they talked at this meeting, they all get a connection to each other, or citation networks are examples of co-occurrence data.
- Text can also be treated as a network, with words serving as nodes and placement in the document serving as the edges. Actions such as steps in a problem solution, or instructional moves, can be treated as a network.
Network methods exist for dealing with various types of data.
Participants will leave the workshop having completed initial data cleaning, analysis, and visualization on their own data set.
Since the syllabus for each field school is adapted to the needs of the participants, the syllabus is only available to registered participants.