Projects
Predicting Use of Narrative in Probability Modeling from Students’ Narrative Identity
Aug 2025 - Dec 2025
This three-part project explored how introductory statistics students employed narrative reasoning while working in a statistical modeling environment known as TinkerPlots. I worked on this project during the fall semester of my junior year alongside my companions Aditi Shrivastava and Jonathan Petro, under the mentorship of Professor Kit Clement. Our work culminated in a polished research product that was presented at the 2025 Master and Undergraduate Research Experience in Statistics (MURES) Symposium.
My contribution focused on the statistical analysis component of the study, where I fit logistic regression models to classroom data to determine whether students’ use of narrative reasoning could be predicted. The abstract and presentation slides are included below for further detail.
Abstract
Traditional inference in introductory statistics often lends students to opt for more efficient, procedure-driven approaches. However, using simulation-based inference and probability modeling can cue narrative forms of explanation. The purpose of this study is to determine how students’ use of narrative versus efficient reasoning is predicted by their own narrative identity. Students completed three in-class sampler activities and a post-activity survey measuring narrative versus efficient reasoning. To measure narrative identity, students were also given the Awareness of Narrative Identity Questionnaire (ANIQ). Logistic regression models were fit for each activity to predict students’ use of narrative. Students’ thematic coherence significantly predicted narrative responses in the first activity, but as students progressed through the learning trajectory, this specific construct was not as important in predicting their use of narrative. This suggests that students’ narrative identity is less relevant in their inferential reasoning as they move through the learning trajectory. This has implications for teaching in how students’ thematic coherence can initially impact their approaches and how to best build students’ conceptual understanding of inference.