This paper is from Session 8A: Research on developing students’ reasoning using simulation methods for introductory statistical inference: Session I

which comes under Topic 8: Research in statistics education

Quantitative evidence for the use of simulation and randomization in the introductory statistics course

Presenter

• Nathan Tintle (Dordt College, United States)

Co-authors

• Allan Rossman (California Polytechnic State University, United States)
• Ally Rogers (Dordt College, United States)
• Beth Chance (California Polytechnic State University, United States)
• George Cobb (Mount Holyoke College, United States)
• Jill VanderStoep (Hope College, United States)
• Soma Roy (California Polytechnic State University, United States)
• Todd Swanson (Hope College, United States)

Abstract

The use of simulation and randomization in the introductory statistics course is gaining popularity, but what evidence is there that these approaches are improving students’ conceptual understanding and attitudes as we hope? In this talk I will discuss evidence from early full-length versions of such a curriculum, covering issues such as (a) items and scales showing improved conceptual performance compared to traditional curriculum, (b) transferability of findings to different institutions, (c) retention of conceptual understanding post-course and (d) student attitudes. Along the way I will discuss a few areas in which students in both simulation/randomization courses and the traditional course still perform poorly on standardized assessments.