Transforming College Students’ Statistical Thinking: Data, Technology & Modeling

An investigation of students’ statistical learning using a modeling and simulation approach.

Lead Staff:
Jennifer Noll
Project Staff:
Andee Rubin


Statistics and data analysis play an increasingly important role in modern society. Without the ability to work with data (e.g. organize, represent, summarize, and model) it is impossible to adequately understand and begin to solve major social issues and make important decisions regarding personal health, finances, and political choices.

This project is developing frameworks for understanding how students learn to represent, model, and organize data as part of their understanding of statistics and data analysis. The project is investigating conjectures made within the statistics education community about the advantages of using technology to teach statistical inference from a modeling and simulation approach as well as investigating the use of technology for data detective work (organizing, representing, and summarizing data).

The collection of rich data gathered in classrooms explores:

  1. The ways students use technology to construct models and run simulations to answer statistical questions
  2. How students use technology to organize represent and interpret data sets

These findings will allow the principal investigator to understand the development of students’ learning of statistical modeling and inform the revision and further design of curricular materials focused on teaching modeling and simulation approaches.

Research Activity

Features of this project include a research-based investigation of new curricular approaches to undergraduate statistics teaching and learning with extensive use of software for learning about data organization, representation, modeling, and simulation. This project should enhance and complement an existing research-based curriculum, CATALST (Change Agents for Teaching and Learning Statistics) that incorporates technology for students’ learning. The data to be collected includes classroom observations and video, student interviews, and assessments of student learning.


Results will include tools for undergraduate statistics instruction to better support statistics teaching and learning for undergraduates. This work will inform educational strategies in the statistics classroom for all students.

Related Work

Noll, J & Kirin, D. (2017). Tinkerplots model construction approaches for comparing two groups: Student perspectives. Statistics Education Research Journal, 16, pp213-243.