The Teaching Statistics group engages in a combination of research, pedagogy, and classroom training. Members are interested in updating and modernizing curriculum and assessment, pedagogical philosophy and development, best classroom practices, student engagement, and outreach to a diverse community.
Current related work includes:
- Summer Teaching Program for our graduate students - training and support for students working as instructors and teaching assistants over the summer
- Teaching Statistics mini: once a semester covering rotating topics
- Redesigning the General Education introductory statistics course to include modern Data Science concepts, active learning, and adaptive material
- Think Aloud studies to design more effective assessment questions for identifying introductory level misconceptions
Faculty, students, and staff are welcome to join and work on projects when available; the below includes current, regular members (faculty, students).
- Rebecca Nugent, Associate Dept Head and Director of Undergraduate Studies, Statistics & Data Science homepage
- Gordon Weinberg, Instructor, Statistics & Data Science homepage
- Christopher Peter Makris, Adjunct Instructor, Programs Administrator, Statistics & Data Science
- Peter Freeman, Special Faculty, Statistics & Data Science homepage
- Howard Seltman, Director of Master's in Statistics Practice program,Senior Research Scientist, Statistics & Data Science homepage
- Philipp Burckhardt, PhD student, Statistics & Data Science/Heinz College
- Peter Elliott, PhD student, Statistics & Data Science
- Justin Hyun, PhD student, Statistics & Data Science homepage
- Kevin Lin, PhD student, Statistics & Data Science
- Amanda Luby, PhD student, Statistics & Data Science homepage
- Josue Orellana Arreaga, PhD student, Center for the Neural Basis of Cognition
- Alex Reinhart, PhD student, Statistics & Data Science homepage
- Jerzy Wieczorek, PhD student, Statistics & Data Science homepage
Below is a partial list of current research/pedagogy projects:
- Adaptive/Active Learning for Early Statistics/Data Science: characterizing students' interests, choices of major, and performance based on data analysis pipeline; how can we build adaptive early statistics and data science curricula for a diverse population that identify their best and likely different individual strategies? How do people choose how to analyze data?
- Identifying Common Misconceptions: using think aloud protocols to determine common misconceptions in early statistics and data science courses and subsequently build better assessment questions
- CMU Pre-College: Data Science Experience: a program of activities and interactive data science experiences designed for high school students, agnostic of background or academic interests
- Eberly Center for Teaching Excellence and Educational Innovation Link
- CMU AP/EA (Pre-College) program, led by William Alba and Veronica Peet Link
- Women in Statistics, CMU Statistics & Data Science Link
- The Carnegie Mellon Data Science Experience: Why take a course in Data Science when you can Experience it?. SIGKDD Impact program, $50K, 2018.
PI: Nugent, Key Personnel: Weinberg, Burckhardt. Project link
- Women in Statistics at CMU: Fostering Collaboration through Formal Mentorship. Carnegie Mellon ProSEED/Crosswalk Program, $1500, 2018.
PIs: Frisoli, Gallagher, Luby (alphabetical order). Grant submitted by Women in Statistics group.
- Burckhardt P, Chouldechova A, Nugent R. The ISLE Experience: Enhancing Classroom Instruction with Interactive E-Learning Tools, CMU Eberly Teaching and Learning Summit, October 2017.
- Burckhardt P, Elliott P, Hyun S, Lin K, Luby A, Makris CP, Orellana J, Reinhart A, Wieczorek J, Weinberg G, Nugent R. Assessment of Student Learning and Misconception Identification in Intro Statistics, CMU Eberly Teaching and Learning Summit, October 2017. Poster
- U.S. Electronic Conference on Teaching Statistics (eCOTS 2018), May 2018
- ASA Symposium on Data Science and Statistics, May 2018
- Joint Statistical Meetings, August 2018