Graduation Year
2023
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Educational Measurement and Research
Major Professor
Jennifer R. Wolgemuth, Ph.D.
Committee Member
John Ferron, Ph.D.
Committee Member
John Skvoretz, Ph.D.
Committee Member
David Lamb, Ph.D.
Keywords
ability grouping, equity, ERGM, sociograms, tracking
Abstract
The dominant philosophy of American public schools has been to group students together based on similar characteristics. Known as tracking, high achieving students would take courses on the “college track” while others would take “career track” courses. It was not long until advocates noticed that this process unfairly advantaged affluent and White student over poor and minoritized groups. A new process called “ability grouping” took over where tracking left off, but to the same effect. It is difficult to measure the degree students are grouped together by a certain characteristic, and while a few research papers aim to do so, none of them have used advanced social network techniques. This dissertation introduces Exponential Random Graph Modeling (ERGMs) and sociograms to the study of curricular networks. My aim is to show how these SNA tools add to the literature regarding ability grouping and tracking within schools. This is possible since as students take classes together, they form connections with one another, which can be measured and visualized. Using ERGMs, I show how to measure the degree to which students are clustered together within curricular networks based on grade level, race, special education need, and academic performance. Using Sociograms, I show how to visualize these curricular networks and glean insight to various structural patterns that emerge. In this dissertation, I highlight how ERGMs and sociograms tell different parts of the same story as well as show how they can be used together. My goal is to provide researchers with a way to measure and visualize middle and high school curricular networks in such a way that avoids the downsides of other methods.
Scholar Commons Citation
West, Geoffrey David, "Using Social Network Analysis to Measure and Visualize Student Clustering Within Middle and High Schools" (2023). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10100
