Statistical Pedagogy & Educational Research
TeachStat Group
The TeachStat Group studies how students learn statistics and data science and how instruction can be improved, from introductory courses to Ph.D.-level topics. Through collaboration between the Department of Statistics & Data Science, the Department of English, and other institutions, its members study student learning, develop new pedagogical techniques, modernize curricula and assessment, and conduct outreach.
Its work has been used to inform education and classroom teaching in Statistics & Data Science courses, in other courses across Dietrich college, as well as at the New Jersey Institute of Technology and other institutions.
Current work includes:
- Understanding how students learn to write about data analysis, how statistical writing differs from other genres, and how best to develop statistical writing skills
- Studying the effect of large language models (LLMs) and generative AI on student learning, and developing productive ways to use AI in the classroom
- Assessing factors related to student success, such as motivation, social embeddedness, and learning behaviors
- Building interactive online tools for teaching statistics and conducting educational research
- Using assessment data and interview studies to study student misconceptions and evaluate teaching interventions
Faculty with this Research Interest
Students with this Research Interest
Collaborators
- Suguru Ishizaki, Department of English, Carnegie Mellon University
- Michael Laudenbach, Humanities & Social Sciences, New Jersey Institute of Technology
- Jerzy Wieczorek, Colby College
- Amanda Luby, Carleton College
- Ciaran Evans, Wake Forest University
Distinguishing the writing style of report introductions written by undergraduate students, published experts, and ChatGPT 3.5.
(DeLuca et al, Journal of Statistics and Data Science Education, 2025)
The distinct writing styles of different large language models (LLMs), relative to human writing.
(Reinhart et al, Proceedings of the National Academy of Sciences, 2025)
Feedback on a student data analysis report generated by the myProse writing studio, which helps students revise their writing to match the expectations of readers.
The writing styles of students’ Data Analysis reports from different years of 36-202 at CMU, compared to ChatGPT’s writing style.
(Colando and Franke, US Conference on Teaching Statistics Research Satellite poster session, July 2025.)
Related Grants
- data.table Ambassador Travel Grant, National Science Foundation (Award Abstract: #2303612), $2,700. Co-recipients: Erin Franke, Sara Colando. Spring 2025.
- Generative AI for Education Tools R&D Seed Grant. “AI-Enhanced Writing Studio for Writing in the Disciplines and Professions.” Suguru Ishizaki, David Kaufer, David Brown, Alan Kohler, Jeremy Rosselot-Merritt, and Gordon Weinberg. 2024-2026.
- Dietrich College Research Seed Grant, $10,000. Supporting the pilot of Docuscope Write & Audit in Statistics & Data Science courses. Fall 2022.
- Simon Initiative Seed Grant, $13,000. “Data-Driven Technology-Enhanced Learning for Statistics and Data Science.” Supporting the development of causality diagrams in introductory Statistics, Summer 2020 - Spring 2021. Alex Reinhart, Ciaran Evans, and Gordon Weinberg.
- Using Think-Aloud Interviews and Cognitive Task Analysis to Identify Misconceptions in Undergraduate Statistics Education. Carnegie Mellon ProSEED/Crosswalk program, $1,000. PIs Josue Orellana and Mikaela Meyer. Spring 2019.
- Women in Statistics at CMU: Fostering Collaboration through Formal Mentorship. Carnegie Mellon ProSEED/Crosswalk Program, $1,700, Spring/Summer 2018. PIs: Frisoli, Gallagher, Luby (alphabetical order). Grant submitted by Women in Statistics group.
Publications
- A. Reinhart. “The regressinator: A simulation tool for teaching regression assumptions and diagnostics in R.” Journal of Statistics and Data Science Education, 2025.
- Z Branson, M Paz Parra, and R Yurko. “The Landscape of College-level Data Visualization Courses, and the Benefits of Incorporating Statistical Thinking.” Journal of Statistics and Data Science Education. September 2025.
- L DeLuca, A Reinhart, G Weinberg, M Laudenbach, S Miller, and D West Brown. “Developing Students’ Statistical Expertise Through Writing in the Age of AI.” Journal of Statistics and Data Science Education 33 (3), June 2025.
- B Markey. “Presenting and making relevant: Analyzing teaching assistant perceptions of writing in Statistics using semantic frames.” IEEE Transactions on Professional Communication 68 (2), 155-172, June 2025.
- A Reinhart, B Markey, M Laudenbach, K Pantusen, R Yurko, G Weinberg, and D West Brown. “Do LLMs write like humans? Variation in grammatical and rhetorical styles.” Proceedings of the National Academy of Sciences, 122 (8), February 2025.
- B Markey, D W Brown, M Laudenbach, and A Kohler. “Dense and Disconnected: Analyzing the Sedimented Style of ChatGPT-Generated Text at Scale.” Written Communication, August 2024.
- M Laudenbach, D W Brown, Z Guo, S Ishizaki, A Reinhart, and G Weinberg. “Visualizing formative feedback in statistics writing: An exploratory study of student motivation using DocuScope Write & Audit.” Assessing Writing, 60, 100830, April 2024.
- Laudenbach, M., Hutchison, A., Guo, Z., & Xu, D.. Structuring Genre Performance for Future Data Scientists. In 2023 IEEE International Professional Communication Conference (ProComm) (pp. 29-32). July 2023.
- M Laudenbach, S Ishizaki, & D W Brown. "Write & Audit: Teaching Genre Features of Statistics Writing with a Student-Facing Text Analysis Tool." In 2022 IEEE International Professional Communication Conference (ProComm), pp 476-481. July 2022.
- A Reinhart, C Evans, A Luby, J Orellana, M Meyer, J Wieczorek, P Elliott, P Burckhardt & R Nugent. “Think-aloud interviews: A tool for exploring student statistical reasoning.” Journal of Statistics and Data Science Education 30 (2), pp. 100-113, July 2022.
- A. Reinhart & C.R. Genovese. “Expanding the Scope of Statistical Computing: Training Statisticians to Be Software Engineers,” Journal of Statistics and Data Science Education 29, pp. S7-S15, 2021. Special issue on Computing in the Statistics and Data Science Curriculum.
- P. Burckhardt, R. Nugent & C.R. Genovese. “Teaching Statistical Concepts and Modern Data Analysis With a Computing-Integrated Learning Environment,” Journal of Statistics and Data Science Education 29, pp. S61-S73, 2021. Special issue on Computing in the Statistics and Data Science Curriculum.
Presentations
- L DeLuca, A Reinhart, M Laudenbach, and D West Brown, “Developing Students’ Statistical Expertise Through Writing in the Age of AI,” Consortium for the Advancement of Undergraduate Statistics Education/JSDSE webinar, September 2025.
- N M D Niezink. “A network perspective on the effect of sense of belonging on academic performance.” Royal Statistical Society 2025 International Conference, Edinburgh, United Kingdom, September 2025.
- A Reinhart. “Do LLMs write like experts? The distinctive style of large language models and their role in student learning.” Session on “Teaching Data Storytelling in the Age of AI,” Joint Statistical Meetings, Nashville, TN, August 2025.
- S Colando and E Franke. “Analyzing Statistics Students’ Writing Before and After the Emergence of Large Language Models.” Poster, US Conference on Teaching Statistics Research Satellite, July 2025. Summary
- P Freeman. “Multinomial Simulations: Demonstrating How We Can (and Why We Should) Use Them in Place of the Chi-Square Goodness-of-Fit Test.” Poster, US Conference on Teaching Statistics, July 2025.
- E Franke and S Colando. “Teaching Data Cleaning and Wrangling with R’s data.table Package.” Poster, US Conference on Teaching Statistics, July 2025.
- R Yurko. “The Landscape of College-level Data Visualization Courses, and the Benefits of Incorporating Statistical Thinking.” Poster, US Conference on Teaching Statistics Research Satellite, July 2025.
- Alex Reinhart. “Writing to learn or learning to write: Writing in the statistics curriculum and the role of LLMs.” ICERM Workshop on Applied Math in Statistics and Data Science Education, Brown University, May 2025. Video
- Laudenbach, M. "The Rhetoric of Generative AI: Leveraging Corpus Methods for LLM Text Analysis." Presented at the University of Washington's Simpson Center for the Humanities research cluster, A Classroom-Centered Inquiry into Generative AI, Large Language Models, and Writing Praxis. April, 2025.
- R Yurko. “SCORE Sports Data Repository.” Contributed talk, Joint Statistical Meetings, Portland, OR. August 2024.
- M Ellingwood. “Finding a statistical voice: Exploring rhetorical structure of student writing from introductory to advanced course levels.” Poster, eCOTS 2024.
- R Yurko, I Ramler, R Nugent, N Clark, M Schuckers, R Lock, and R Strudivant. “Creating and Sharing Sports Data Content with the SCORE Network.” Breakout session, eCOTS 2024.
- Ishizaki, S., Laudenbach, M., & Brown, D.W. "Supporting Student Writing and Reasoning in Data Science Using a Scalable Automated Feedback System," Institute for Mathematical and Statistical Innovation. Chicago, IL. January 2024.
- R Yurko. “CMSACamp: A Summer Undergraduate Research Experience with Sports Analytics.” Contributed talk, Joint Statistical Meetings, Toronto Canada. August 2023.
- A Reinhart, C Evans, & A Luby. Think-Aloud Interviews: A Tool for Exploring Student Statistical Reasoning. Consortium for the Advancement of Undergraduate Statistics Education/JSDSE webinar, June 2022.
- C Evans, A Reinhart, P Burckhardt, R Nugent, & G Weinberg. Exploring how students reason about correlation and causation. eCOTS 2020. Poster
- M Meyer, J Orellana, A Reinhart. Using Cognitive Task Analysis to Uncover Misconceptions in Statistical Inference Courses. eCOTS 2020. Poster
- P Burckhardt, P W Elliott, C Evans, A Luby, M Meyer, J Orellana, J Wieczorek, R Nugent & A Reinhart. Writing practical pre- and post-tests for concepts in introductory courses, CMU Eberly Teaching and Learning Summit, November 2019. Poster.
- M Meyer, J Orellana, A Reinhart. Using Think-Aloud Interviews and Cognitive Task Analysis to Identify Misconceptions in Undergraduate Statistics Education, Joint Statistical Meetings, July 2019. Poster
- P Burckhardt, C Genovese, R Nugent, R Yurko. Incorporating Real-Time Clustering of Student Responses into an E-Learning System, Joint Statistical Meetings, July 2019. Poster
- A Reinhart, P Burckhardt, P W Elliott, C Evans, A Luby, M Meyer, J Orellana, R Yurko, G Weinberg, J Wieczorek & R Nugent. Using think-aloud interviews to assess student understanding of statistics concepts. Breakout session. US Conference on Teaching Statistics (USCOTS), May 2019. Abstract, slides, handout
- R Nugent, R Yurko, P Burckhardt & F Kovacs. "Many Students, One Dataset": Using ISLE to Teach Reproducibility and the Impact of Data Analysis Decisions on Conclusions. Breakout session. US Conference on Teaching Statistics (USCOTS), May 2019. Abstract
- P Burckhardt, P W Elliott, C Evans, S Hyun, K Lin, A Luby, C P Makris, M Meyer, J Orellana, R Yurko, G Weinberg, J Wieczorek, R Nugent & A Reinhart. Developing an assessment for concepts in introductory statistics and data science. CMU Eberly Teaching and Learning Summit, October 2018. Poster. (People's Choice Award winner)
- Burckhardt P, Nugent R, Genovese C. How students make sense of data on an e-learning platform. Joint Statistical Meetings, Vancouver, July-August 2018.
- Burckhardt P, Chouldechova A, Nugent R. TeachIT: Turning the Classroom into a Research Laboratory via Interactive E-Learning Tools. Invited paper. Proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July, 2018), Kyoto, Japan. Paper
- Yurko R, Nugent R, P Burckhardt. Detecting Data Analysis Patterns in Text and Graphs to Characterize Student Learning in an Introductory Statistics & Data Science Course, Classification Society Annual Meeting, June 2018.
- Yurko R, Nugent R. Using text analysis to characterize student learning in an introductory statistics & data science course, Electronic Conference On Teaching Statistics (eCOTS), May 2018. Video poster
- Kayla Frisoli, Gordon Weinberg, & Rebecca Nugent. “Building early data science tools for a diverse audience”. Electronic Conference on Teaching Statistics (eCOTS), May 2018. Video poster
- S Hyun, P Burckhardt, P Elliott, C Evans, K Lin, A Luby, C P Makris, J Orellana, A Reinhart, J Wieczorek, R Yurko, G Weinberg & R Nugent. Identifying misconceptions of introductory data science using a think-aloud protocol, Electronic Conference on Teaching Statistics (eCOTS), May 2018. Video poster
- Burckhardt P, Nugent R, Genovese C. Learning Data Science with the Help of a Data Exploration Tool. Electronic Conference On Teaching Statistics (eCOTS), May 2018. Video poster
- 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