Teaching Statement
Alex Newhouse
Quantitative and computational methods become most powerful when students understand why they matter, not just how to execute them.
I am a product of the liberal arts. I graduated summa cum laude from Middlebury College, and I experienced firsthand how rigorous small-classroom teaching can change how students think about the world. That experience shapes how I teach political science: my courses are organized around political questions worth answering, with methods introduced as the tools that make those answers possible.
Methods teaching begins with intuition
In quantitative methods courses I structure lessons around real political science questions. Students do not learn regression in the abstract; they learn it by analyzing data on political violence, public opinion, and democratic institutions, where a coefficient has a meaning they can argue about. Statistical reasoning is easier to acquire when it is anchored to intuitions students already have about politics.
To support that approach I write original, open teaching materials for students with no prior coding experience — from Greek notation and descriptive statistics through dplyr verbs, variable transformations, and the file-system problems that quietly derail beginners. The tutorial series is used in CU Boulder’s PSCI 2075 and is freely available for other instructors to adapt.
Scaffolding and inclusive pedagogy
Quantitative methods intimidate students who have decided they are “not math people.” Breaking down that barrier is a design problem, not a motivational one. I use scaffolded assignments that introduce complexity in deliberate increments, I build a classroom where asking a basic question carries no cost, and I create multiple entry points into the same material. My tutorial on using AI chatbots to learn R is one example: it takes seriously that students already use these tools, and teaches them how to do so without short-circuiting their own learning.
Research in the classroom
Each of my self-designed courses has included a substantial original research component. In The Study of Post-War Fascism at Middlebury College, students developed original research designs on topics in contemporary fascism studies, learning substantive expertise and methodological craft at the same time. In Digital Extremism at MIIS, students conducted their own analyses of extremist communities across platforms. Courses like these also require teaching research ethics directly: how to study sensitive political topics without amplifying them, and how to handle data about real people responsibly.
Mentorship
Since 2019 I have mentored 52 undergraduate and graduate students as research assistants, interns, and fellows, and supervised 15 independent student projects — on Italian neofascism, militant accelerationist coalition-building, the French far right on encrypted platforms, and the implications of AI for counterterrorism, among others. My aim in mentorship is to move students from executing tasks to owning questions: assistants start with annotation and data preparation and, where they want to, finish with a project and an argument of their own.
Courses I am prepared to teach
- Introduction to Political Science / American Government
- Introduction to Comparative Politics
- Quantitative Research Methods (introductory and advanced)
- Data Science for Social Scientists
- Political Violence and Extremism
- Technology, Media, and Politics
- Senior Seminar / Capstone in Computational Social Science
A full PDF version, along with teaching evaluations, is available on request — alex.newhouse@colorado.edu. See also the teaching page and a sample syllabus.
Last updated: August 2026