Teaching

In my approach to teaching, I challenge students to evaluate the way they think about the world around them. To do this, I prioritize active learning inside and outside the classroom coupled with discussions anchored in current events. This teaching methodology encourages critical thinking on the causes and effects of day-to-day political phenomena, producing students who can ask critical questions and provide thoughtful solutions. During my tenure in graduate school I have served as instructor of record for undergraduate courses in political methodology and American politics. I also served as a teaching assistant for three semesters. Student evaluations of my teaching, both as an instructor and teaching assistant can be accessed here. In recognition of my efforts inside and outside of the classroom, I was received the Earle Wallace Award for Graduate Student Teaching from UNC’s Political Science Department in 2019.

To make my expertise more accessible outside the traditional classroom setting, I teach a two-day seminar through the Odum Institute for Research in Social Science at UNC-Chapel Hill on collecting and analyzing text data in R. Short courses like mine are designed as a means of continued education for professionals, faculty, and UNC students. I specifically tailor this class to address the types of research questions that participants want to answer. I make all my class materials publicly available on GitHub. These experiences have prepared me to offer courses in American politics and political methodology at both the undergraduate and graduate levels.

The University of North Carolina at Chapel Hill

  • Poli 281: Quantitative Research in Political Science (Fall 2019; Spring 2020; Fall 2020)
    • The course is target towards undergraduate students with interest in the social sciences, who want to use quantitative approaches to solve important problems as well as develop marketable analytical skills. This course is designed to achieve three objectives: (1) introduce students to research and quantitative analysis in political science, (2) produce critical consumers of quantitative analysis used in political and policy-oriented reporting, and (3) give students the ability to answer questions of social scientific importance using data. Over the course of the semester, students learn and become proficient in the statistical programming language R.
    • Syllabus
    • Example Slides
    • Critical Analysis Project Overview
  • Poli 100: Introduction to American Politics (Spring 2019)
    • In order to fully understand our complicated and contentious political climate and events, it is necessary to understand how the American political system works. We will work toward that understanding together by exploring the system from two angles: institutions, or the structure of the United States government; and behavior, or the actions and motivations of political actors, both politicians and the general public. We will discuss what problems the government and political system seek to solve and why they sometimes succeed and sometimes fail to solve those problems.
    • Syllabus
    • Example Slides

Odum Institute for Research in Social Sciences at UNC

  • Collecting and Analysing Text Using R (Spring 2019; Fall 2019; Spring 2020; Fall 2020)
    • This workshop will introduce participants to the basics of text analysis in R. Text analysis is a promising new approach that uses machine learning to discover patterns, trends, and other information by using text as data. For example, in measuring misinformation on social media to study how candidates have changed their campaign rhetoric over time, text analysis has an incredibly broad scope of applications. In this two day, five hour course, participants will develop the skills and understanding necessary to collect text data from a variety of sources, including websites and APIs. Participants will gain practical skills for completing their own analyses using text as data. Basic to intermediate knowledge of programming in R is required; no background in statistics is necessary.
    • Text Collecting and Processing
    • Sentiment Analysis