I am an assistant professor of research methods, measurement, and evaluation currently at the Neag School of Education at the University of Connecticut. As an educational methodologist, I am committed to identifying effective educational programs and policies. I specialize in leveraging AI to extract insights from complex or hard-to-access data, including unstructured data on program implementation (like transcripts and participant writing) . In doing so, I address critical resource constraints in education research while simultaneously addressing risks in the use and interpretation of AI output.
I am currently taking on PhD students. Please email me and/or apply if you are interested.
In recent research, I have:
Developed a research protocol for increasing the validity of machine learning applications in education research
Provided an overview of zero and few-shot classification, with a focus on validitation
Developed scalable methods for collecting local policy data from school district websites and used that data to estimate the impact of wide-scale deregulation.
Developed methods for measuring fidelity in standardized educational interventions
Developed methods for improving human labeled training data for machine learning classifiers.
You can see a complete list of my publications here.
I became interested in evaluation and implementation when teaching middle schoolers. As a teacher, I wanted to know what programs would work for my students and our circumstances, not the average student in the average circumstances. Today, I address these questions by helping researchers identify valid program impacts while paying attention to variation in implementation and outcomes.
Anglin, Kylie. “Addressing Threats to Validity in Supervised Machine Learning: A Framework and Best Practices for Education Researchers.” AERA Open 10 (January 1, 2024): 1–21. https://doi.org/10.1177/23328584241303495.
Anglin, Kylie L., and Claudia Ventura. “Automatic Text Classification with Large Language Models: A Review of Openai for Zero-and Few-Shot Classification.” Journal of Educational and Behavioral Statistics, 2024, 1–23. https://doi.org/10.3102/10769986241279927.
Anglin, Kylie. “The Role of State Education Regulation: Evidence from the Texas Districts of Innovation Statute.” Educational Evaluation and Policy Analysis 46, no. 2 (2024): 534–54. https://doi.org/10.3102/01623737231176509.
PhD Educational Policy, 2021
University of Virginia
Masters in Public Policy, 2018
University of Virginia
Post-Baccalearate in Mathematics, 2015
Northwestern University
BA in Political Science, 2013
Southwestern University University