With Lisa Dierker (Wesleyan University)
Wednesday, May 17 at 1:00 p.m. to Thursday, May 18 at noon
This one-day workshop will support instructors who teach an introductory statistics or quantitative research course in designing or redesigning any or all portions of their course to engage students in the rich, complicated, decision-making process of real statistical inquiry. Core features of this approach include providing opportunities for students to flexibly apply their statistical knowledge in the context of real data, the use of computing as a window to core statistical concepts, supporting students with varying levels of preparation, and attracting and inspiring students from underrepresented groups.
The workshop will include very brief presentations focused on the nuts and bolts of supporting project-based experiences, followed by ample hands-on opportunities that will be supported by experienced faculty and students. Similar to the approach that will be presented; your experience in the workshop will be individualized to your own interests, background and needs.
You are welcome to bring your preferred statistical software or may use the cloud-based SAS Studio, requiring only an internet browser. Supporting materials will be made available. Personal laptops are required. The workshop is intended for instructors at all levels, regardless of discipline-specific training (e.g. math, statistics, biology, political science, psychology, sociology, education, epidemiology, geology, etc.). This curricular approach has been used at many colleges and universities (e.g. Wesleyan University, University of New Mexico, Theil College, Ashesi University -Ghana, Southwestern Oklahoma State University, Virginia Tech, Appalachian State University, Concordia University - Texas, Emory College, Naugatuck Valley Community College, SUNY - Purchase) and has recently been introduced to students at the high school level at Scarsdale High School, NY and through the GEAR UP Program http://passiondrivenstatistics.com/2016/09/21/gear-up/.
Supported by NSF DUE # 1323084