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This is a session of Topic 8: Research in statistics education

(Monday 14th, 16:15-17:45)

Research on developing students’ reasoning using simulation methods for introductory statistical inference: Session 2



In his landmark paper in 2007 (TISE), George Cobb argued for a 21st century approach to teaching introductory statistics. He advocated for the use of randomization and simulation methods for instruction in statistical inference, rather than the traditional formula-based approach (using methods such as the t-test and ANOVA).

Since that time, extensive research has been undertaken to develop, implement, and formally evaluate these computational approaches to teaching statistics. In addition, there has been a slow but steady incorporation of randomization and bootstrap methods topics into textbooks at the introductory level.

This session features speakers who have been instrumental in leading research to assess the impact of simulation methods on students’ statistical reasoning. They will describe and summarize the state of the art of research related to the use of simulation methods in the introductory statistics curriculum. A primary focus will be on the impact of simulation methods on students’ statistical reasoning, as well as discussion of barriers and aspects that require additional study.


PaperTitlePresenter / Co-author(s)
8B1The symbiotic, mutualistic relationship between modeling and simulation in developing students’ statistical reasoning about inference and uncertaintyAndrew Zieffler (United States)
Ethan Brown (United States)
Robert C delMas (United States)
Joan Garfield (United States)
8B2Bootstrapping for learning statisticsTim Hesterberg (United States)
8B3From data to decision-making: using simulation and resampling methods to teach inferential conceptsMia Stephens (United States)
Robert H Carver (United States)
Don McCormack (United States)