Sample Syllabi

The first syllabus is for Social Psychology, an introductory breadth course open to majors and non-majors, spanning classic and contemporary research. In my version of this course, I incorporate my expertise as a developmental psychologist, teaching my students about what we think, how we behave, and when these patterns emerge across the lifespan. My central objective for Social Psychology is for my students to leave with better scientific literacy. One way I achieve this is through the norms I instill in the classroom: I encouraged students to insightfully critique the work presenting. They should not only ask whether the evidence was convincing but also be critical of their own skepticism. I tell my students to ask themselves if and how their concerns would affect the results, which results in very thoughtful conversations about the work. I also achieve this goal through my assignments. For the final project, students choose an empirical article and share it with the class. With their new skills, they were able to identify many fascinating challenges with their research articles and proposed a future direction to resolve their qualms. For context, I taught this as an intensive course during the summer session, so it met every day for 3.5 hours, for 3 weeks. If I were to conduct this course an academic term, I would divide the existing lectures in half for each class meeting, and I would also like to add lectures on other topics such as open science practices and The Replication Crisis.

The second syllabus is for How and Why We Win Friends and Influence People, an advanced seminar of my own design. The course takes an interdisciplinary approach, drawing on social, evolutionary, and developmental psychology to examine the science of human connection and social influence. Its central learning objective is developing experimental design skills, which I cultivate in two ways. The first is critical engagement with existing research. Students learn to identify the key elements of empirical studies (independent and dependent variables) and are consistently pushed to evaluate what those designs can and cannot tell us. The second is generative thinking. Students don't just analyze research; they practice imagining it. Throughout the course, they encounter regular invitations to ask, “How could I test that?” This culminates in a lab meeting simulation, where students arrive with deliberately half-formed ideas and work together to develop them into a testable research question.

I am especially eager to teach my own Developmental Psychology course. Similar to Social Psychology, Developmental Psychology is an introductory breadth course that is often open to majors and non-majors, and it covers classic theories and research regarding child development. My objective for this course would be for students to leave understanding developmental psychology as more than just a list of milestones, and more like a scientific discipline. Every unit pairs a lecture on a developmental phenomenon with a Friday discussion off primary research behind it. In Week 1, we will work on learning to read these articles by completing a guided worksheet as a class, so students can read and evaluate scientific claims on their own. For their final project, students think about how they can use scientific methods to address the questions they raised throughout the term. 

I would love to teach Statistical Methods in Psychology. I used to have a lot of math anxiety, and was afraid of taking my graduate statistics courses and learning to code, but I learned I could do both. Helping own introductory statistics students at UChicago overcome these same fears has been one of the most rewarding experiences of my teaching career.

My experience teaching introductory statistics has heavily informed my approach to Statistical Methods in Psychology. After each lecture, students participate in collaborative learning activities to solidify their own learning and help each other. After class, students complete problem sets, which they self-grade. They are asked to identify what they got wrong, explain why, and retry. For their final project, students preregister hypotheses, run the statistics by hand and in R, write an APA-style results section, and present their findings in a data blitz. I can't wait to watch students go from being afraid of the R installation process to presenting their own regressions to their peers.