Students learn about the world around them through engagement with big questions that are real but can be solved through social science research methods. The two keywords that summarize my teaching are contextual and active. Data science and health analytics should be taught in the context of natural (and student-identified) issues to make the subject more relatable and exciting for students. Students should also be active and learn by doing through a project-based curriculum where they apply course context to their personal experiences. This project-based curriculum allows them to develop their critical and creative thinking skills as a liberally educated student.

Based on my experiences thus far, I am exceptionally optimistic and accepting of the quality and motivations of undergraduate and graduate students. When I have had the opportunity to teach, I integrate my research and scholarly interests into the classroom. This results in an enthusiasm for teaching and the chance to recruit students as research assistants.

I use a flipped classroom model at the undergraduate level to make it easier for students to remain active during class time. Lectures and readings are completed at home. Then, the classroom time is used to apply the material in activities, games, and role plays that lead to deeper learning. Every class is a new experience and a celebration of discovery together. I have banned exams in favor of celebrations of learning, emphasizing how much students have grown and away from scores that they have earned. 

When working with graduate students, I use discussion leaders and small group exercises to ensure subject-centered learning, community, and mentorship. This level also allows me to integrate my scholarship more closely, exposing graduate students to research through instruction, reinforcement, and internalization. In addition, I structure course assignments and the final exam to support drafting a publishable journal article.

At both levels, students use real-world datasets that will be useful for their careers, like patient-level claims datasets from the Centers for Medicare and Medicaid Services. I would also be interested in partnering with a local hospital system to use HIPPA-protected data for classroom instruction and student research projects as ‘external consultants’ focused on improving readmissions, patient safety, costs, and patient outcomes. Students interested in data science and health could complete this course and strengthen their understanding through an ILE opportunity in health data analytics.

I also firmly believe in professionally oriented educational opportunities, which provide advanced technical knowledge in the applied sciences. These professional degrees and certificate programs bridge academic and industry needs to enhance students’ employability after graduation. I have supported the development of modules in health data analytics for both undergraduate education and a professional certificate program.

I look forward to seeking professional development opportunities for teaching through reading and observing high performers across campus as I adjust to larger classroom sizes and a broader diversity of student backgrounds outside the private university.

Student mentorship and supervision. I practice a student-centered approach in graduate and undergraduate supervision, conveying knowledge and cultivating each student’s unique intellectual capacity. Recognizing that students learn best when they feel valued, I practice relationship-based mentorship. This approach focuses on building a personal, ongoing connection that emphasizes mutual respect, clear communication, and understanding of each other’s goals, values, and needs.

I use consultant and lab-based models for supervising student research. The consultant approach empowers students to initiate research that fits my expertise, allowing me to lead, guide, and aid them in their journey. The lab-based model, derived from my experience in the applied sciences, will enable me to expand opportunities to more students through a distributed workload that reduces publication time. Graduate students who work actively with me receive fair compensation for their labor because of my ability to secure funding for their development and integration into research.

To cite a specific example (under review), I designed an Introduction to Sociology course for pre-medicine majors at Baylor University that included 15 hours of experiential learning to strengthen their cultural competency skills. Students participated in telephone and door-to-door data collection for a Community Health Needs Assessment. Overall, the 65 undergraduate students I supervised developed a more robust understanding of the social determinants of health through hands-on learning opportunities in the community.

Furthermore, my commitment to student success extends beyond research and into teaching. I’ve supported graduate student teaching by providing regular feedback, sharing effective teaching strategies, and fostering a collaborative environment. I also serve the broader campus by assisting with the Graduate Academy for College Teaching taught by CITL for students with classroom responsibilities.

I also support experiential learning programming by hosting undergraduate and graduate students completing experiential learning experiences. I am familiar with their educational needs and range of experiences, working meticulously to ensure they have a meaningful internship experience for their long-term interests. I am interested in providing service in the future for graduate students completing research projects for course credit in their respective programs.

My languages of choice for instruction are SAS, R, Python, Apache Spark (Python) and Hadoop (SAS), and SQL.