TEACHING PHILOSOPHY

As an instructor and mentor in psychological science, my goal is to help students become more independent thinkers, researchers, and problem-solvers. I think of teaching as helping someone do something they could not do before and helping them trust that they can keep doing it without me. This approach is shaped partly by my own experience as a learner. Early in my education, I thought of myself as someone who was not good at math. Learning that statistical methods can be more intuitive when they are connected to meaningful questions changed how I understood myself as a student, and it now shapes how I teach. I want students to see psychological science not simply as a body of findings to memorize, but as a set of tools they can use to ask better questions, evaluate evidence, and understand their own experiences and the social world more deeply.

My teaching experience spans introductory psychology, graduate-level statistics and methods courses, and undergraduate research mentorship. I have taught Introduction to Psychology, served as a teaching assistant for graduate courses in meta-analysis, multilevel modeling, and advanced statistics, and mentored undergraduate research assistants through data collection, coding, analysis, and conference presentations. Across these settings, my teaching is guided by three commitments: making material personally and practically relevant, building skills through structured practice, and meeting students where they are while helping them move toward independence.

Making Material Personally and Practically Relevant

My strongest conviction about teaching is that relevance, not simplification, is what makes material stick. This is especially important when teaching statistics and methods, where students often approach the material with anxiety or with the assumption that quantitative tools are separate from the questions they actually care about. As a teaching assistant for graduate courses in multilevel modeling, meta-analysis, and advanced statistics, I make a point of learning what students are studying and, whenever possible, rebuilding explanations around their own research questions and data. For example, in a multilevel modeling course, a student in the health psychology area was struggling to distinguish fixed from random effects and how to build their model. Once we worked through the distinctions using their own variables, the model structure clicked in a way that generic notation had not. What changed was not the statistical concept itself, but the reason it mattered. They already cared deeply about modeling correctly, so the analytic decision became meaningful rather than procedural. Experiences like this reinforce my belief that students learn methods best when they can see how a statistical decision changes the claims they are able to make.

I bring the same approach to Introduction to Psychology. When teaching topics such as motivation, goal setting, self-regulation, and study habits, I draw on my own research areas to show students how psychological science applies to behaviors they already engage in every day. Rather than treating these topics as common-sense advice, I present them as empirically grounded tools. For example, explaining why a specific, approach-oriented goal is more likely to support goal progress than a vague aspiration helps students connect research on motivation to how they might practically structure their studying, exercise goals, or time management. For many students, this is the first time they have seen the science behind everyday choices. My goal is to make the material matter to each student.

Building Skills Through Structured Practice

I also believe students learn best when complex skills are broken into visible, manageable steps and then practiced with increasing independence. In training undergraduate research assistants in the Self-Regulation Lab, I use a “see one, do one, teach one” model. New lab members first observe a procedure, such as running a study session, coding behavioral video, or cleaning a dataset. They then complete the task under supervision, and once they are ready, they help train the next incoming research assistant. Each stage serves a different purpose. Observation lowers the stakes of a first attempt, supervised practice builds competence while support is still available, and teaching consolidates the skill while also developing the mentee’s confidence and communication.

This same model informs how I approach classroom teaching, especially in statistics and methods courses. Students need more than exposure to concepts; they need opportunities to apply them, make mistakes, receive feedback, and try again. In practice, this means pairing conceptual explanations with worked examples, guided practice, and then independent or peer-supported application. For a statistics course, that might involve first walking students through a model, then having them interpret output together, and finally asking them to apply the same logic to a new research question. For a research methods course, it might involve moving from identifying flaws in published studies to designing stronger studies of their own. In each case, the goal is to help students develop transferable skills rather than isolated pieces of knowledge.

Meeting Students Where They Are

Students enter psychology courses with different backgrounds, levels of preparation, and sources of confidence or anxiety. This is especially true in statistics and methods courses, where some students arrive with strong quantitative training and others arrive convinced that they “don’t like math.” I try to understand where a student is before deciding what kind of support they need. Sometimes a student is confused about the concept itself; other times they understand more than they realize but need help translating that understanding into language, notation, or code. My job is to diagnose the barrier and provide the right scaffold.

Meeting students where they are does not mean lowering expectations. It means giving students the support they need to meet high expectations. In office hours and informal mentoring conversations, I often rephrase a statistical idea several ways until one explanation lands. With research assistants, I try to match tasks to current strengths while still helping them grow into more complex responsibilities. For one student, that might mean beginning with structured data coding before moving into analysis. For another, it might mean taking on more responsibility in study coordination or helping prepare conference materials. These adjustments help students build competence while still contributing meaningfully to the research process.

Some of my most productive teaching moments have occurred outside formal class time, including conversations with students who sought me out after guest lectures to talk through their own research ideas or interests in neuroscience and psychology. These moments matter because they are often when students begin to see themselves as participants in psychological science rather than just consumers of course material. I view mentorship as an extension of teaching, because it allows students to practice the habits of mind that courses are meant to develop: asking clear questions, thinking critically about evidence, and revising ideas in response to data.

Looking Forward

As I continue developing as an instructor, I am especially excited to teach research methods, statistics, and courses related to social cognition, self-regulation, motivation, and emotion regulation. My methodological training in multilevel modeling, longitudinal design, meta-analysis, and large-scale replication positions me to contribute to methods and statistics instruction in psychology, particularly for students who are learning to connect quantitative tools to their own research questions. I also see teaching as closely tied to my broader research identity. Because my work examines motivation, self-regulation, and social cognition, I aim to teach students not only what psychological science has found, but how psychological scientists build, test, and refine those ideas.

Across courses and mentoring contexts, I want students to leave having done something themselves, understood why it mattered, and felt more capable of doing it again. Whether I am teaching introductory psychology, mentoring undergraduate researchers, or helping graduate students work through statistical models, my goal is the same: to help students build the confidence and skills to use psychological science independently.