A Research-Based Statistics Course for Tertiary Students
Jiyoon Park, Robert delMas, Andrew Zieffler, Joan Garfield
Educational Psychology, University of Minnesota, Minneapolis, MN, United States

The NSF-funded CATALST project has developed a radically different undergraduate introductory-statistics course that uses randomization and resampling approaches as the only methods for statistical inference. This course is based on research in cognitive science, mathematics and engineering education, as well as in statistics education. A carefully designed sequence of activities was used to enable students tp develop their understanding of randomness, chance models, randomization tests and bootstrap coverage intervals. For each unit in this course, students first engage in a Model-Eliciting Activity (MEA; Lesh & Doer, 2003; Zawojewski, Bowman, & Diefes-Dux, 2008) that primes them for learning the statistical content of the unit (Schwartz, 2004). The MEAs are also used to promote statistical thinking. Activities follow each MEA where the students explore how to model chance and chance models using TinkerPlots modeling software.The results of a year long teaching experiment studying two offerings of this course will be presented.

Keywords: Introductory-statistics course; Randomization and resampling; Statistical inference; MEA

Biography: Jiyoon Park is a Ph.D. student in the Statistics Education Program at the University of Minnesota. This is her third year of studying statistics education. She got her Master's degree in mathematics education at UT-Austin after she taught mathematics in a high school in Seoul, and came to Minnesota in 2008. Her research interests are statistical inferential reasoning of college students, development of a test for assessing statistical reasoning based on measurement theory, and development of educational statistical tool using R.