Teaching statement
Overall teaching philosophy
Research and education go hand-in-hand. Well-taught, empowered students make for effective and independent new researchers, who in turn make for more self-motivated and ambitious students and trainees. Effective teaching builds technical and theoretical competency in the evaluation, planning, and execution of scientific research that prepares students to hit the ground running when they begin research. Prepared students work faster and create higher quality work, which grants a sense of accomplishment and progress that helps sustain their motivation through tougher periods of work. Effective teaching also builds interest in the discipline being taught among the student body. When students build concrete skills through their coursework, they can more easily see themselves as researchers using those skills, increasing their confidence enough that they may choose to pursue research opportunities with their professor or similar researchers.
For research faculty, these skills and interest in research are important traits to foster in a graduate or undergraduate program. Overall interest in the program will grow, fueling a stronger applicant pool for graduate programs and research labs, leading to trainees who can more effectively contribute. Solid skills-oriented teaching, then, is a crucial component for sustained success as a research faculty member.
Approach to teaching and mentoring
Through mentoring, guest lecturing, and participation in the University of Wisconsin Teaching Academy as an affiliate member, I have been able to practice and solidify my philosophy of teaching. Two of the most important concepts underlying my approach to teaching and mentoring are backward design and scaffolding. Backward design emphasizes the identification of end goals first and then creating learning experiences and tasks to attain those goals. Scaffolding, on the other hand, is the intentional progression of tasks that slowly lead to student independence. For a field as interdisciplinary as epidemiology and bio-data science, these education techniques are powerful ways to build competency quickly and effectively across a diversity of skills.
As an example, in preparing my material for a guest lecture on genetic analysis techniques in the Genetic Epidemiology course at the University of Wisconsin-Madidson, I used a backward design approach for my lecture. I began by identifying the most important information students needed to know about polygenic risk score analysis and Mendelian randomization, and then I designed my lecture to emphasize those key points clearly and repeatedly. One specific example: to ensure that students walked away with an understanding of when you would and would not want to use a polygenic score analysis, I designed a think-pair-share activity to allow students to think through the potential benefits and issues on their own before discussing the answers as a group. In this same class, we used a scaffolding approach to design their practical lab for genetic data cleaning and analysis. We began with a simple tutorial to get students acclimated to Unix server navigation, then gave students simple data cleaning tasks to execute with a carefully curated data set for them to use, and then finally gave students larger assignments to conduct a complete genetic analysis similar to what one might do in practice. Although many students entered the class with no previous experience with Unix servers and genetic data, they left the practical lab able to complete a genome-wide association analysis.
I have approached my mentorship of undergraduate students with a similar backward design and scaffolding framework. I begin research projects with my mentees by first identifying what their objectives are for their rotation and how to align those with the needs of the lab. Then, I plan for them a series of progressive tasks and responsibilities to meet those objectives in the time available. For instance, with one student, we identified our goal to be the implementation of several different genetic prediction models for metabolites. I assessed what skills they needed to learn to complete this task and then created a skills-building schedule, with a mix of topical, one-on-one discussions and self-guided tutorials for the student to acquire the skills they needed in time to complete the task in the semester, which they were able to achieve.
