“Too many students today are learning that ‘science’ is a set of facts and procedures that are unrelated to the workings of the world and are simply to be memorized without understanding, and they learn to ‘solve’ science problems by memorizing recipes that are of little use other than passing classroom exams.”

Carl Wieman, Nobel Laureate, Physics

Active Learning Philosophy

To improve the state of mathematics and science education, Wieman developed an “active learning” philosophy initiative (known as CWSEI). This approach utilizes evidence-based teaching methods to improve learning outcomes. The philosophy is described in detail here.

This active learning philosophy guides my pedagogical approach, which emphasizes student engagement through three core features. First, students must know exactly what they should learn in the class. Second, instructors must actively gather data to assess the students’ mastery of these goals. Third, instructors must adapt their course according to what they see in this data. To accomplish these goals, I implement several important features of active learning in my undergraduate statistical analysis course.

Undergraduate Statistical Analysis

Some of the active learning features that I implement include clearly defined learning objectives, in-class demonstrations and examples, small group problem sets, and a mid-quarter (or session) review. Please see the CWSEI Teaching Practices Inventory for my course for more information about which methods I use.

Statistical Analysis Syllabus

Example Lecture

CWSEI Teaching Practices Inventory Scoring

Course Evaluation Scores

Future Courses

I look forward to developing additional courses in the areas of medical sociology, global health, and the political economy of health. I also would enjoy teaching undergraduate and graduate statistics and methods (including advanced methods, such as Social Network Analysis).