As a Core Curriculum Teaching Fellow, my project will develop a new course called “Statistics for Research” focusing on innovation, undergraduate research, and writing across the curriculum as an alternative to the traditional Introduction to Statistics, Stat 145. Intro Stats is one of the largest courses on campus with 1000-1200 students per semester in over 20 sections. This Statistics for Research course will cover the traditional material, but in a modern evidence-based way by integrating real data in the context of case studies, fostering active learning, and using technology to explore concepts and analyze large real datasets and communicate the results. This is a computer- and a project-based course in the style of Dierker’s Passion-Driven Statistics and modeled directly after the already-successful UNM Stat 427/527 Advanced Data Analysis 1 and 2 where I’ve piloted these learning strategies for advanced undergraduates and graduate students for four semesters. Unlike my previous six-section intervention study showing active learning increases success by a third of a letter grade for everyone, this will be a pilot course developed for one or two sections. This implementation is just one of the many evidence-based recommendations I made as a 2016-17 Teaching Fellow for improving statistics education at UNM, including offering multiple versions (Stat Literacy with a first-year learning community, Stats for Research, Advanced Math-Stats); train/mentor our TAs prior to their teaching a course; offer a new Undergraduate Stat Ed Practicum course for all undergrad stat majors to serve as peer learning facilitators in at least one intro stat course; use multidisciplinary project-based learning to improve the outcomes of statistical interest, attitude, and condence for under-represented minority students; and make other small changes such as moving the final from the first day of finals at 7:30 AM to a later time when students are shown to have greater success. A long-term goal is to hire a professor of practice in statistics for continued leadership and research in statistical education at UNM.
- If you already have a set of functions that you load with script(“my_functions.R”), then you’re an afternoon away from making a great package.
- If you have a large script with repeated code, then you can start by turning the repeated code into functions, package those functions, and write a short vignette to perform the same analysis using your package.
- If you have an idea for a new package to develop, bring that idea with you and we can consider trying to develop it during the workshop.
- My project is to package all the code and data for my two-semester data analysis course while I’m not helping others.
- learn more about Bayesian graphical models,
- learn more about Hamiltonian Monte Carlo (HMC),
- learn about their statistical and computational implementations, and
- apply both to extend current models in the application to fMRI brain imaging data.
- Continue UNM 100-level statistics and mathematics education initiatives to understand factors influencing student success and find strategies to increase success.