Teaching Statistics with R
A 2-day workshop preceding USCOTS taught by Danny Kaplan (Macalester College), Nick Horton (Smith College), and Randy Pruim (Calvin College)
Supported by NSF DUE # 0920350
Dates: Tuesday afternoon through Thursday morning, May 17 to May 19.
Lodging: One nights lodging will be paid directly to the Embassy Suites hotel for those workshop attendees who need lodging.
Abstract: This two-day workshop, held in conjunction with USCOTS, will introduce participants to teaching a course in applied statistics that uses computing in an integrated way. Some of the topics and techniques we introduce are suitable for introductory students, some make sense for students in a "second" or even higher-level statistics course. At all levels, the emphasis will be on statistical modeling with data, applications, and on computation with R. The workshop is designed to be accessible to those with little or no background in R, and will provide you with skills, examples, and resources that you can use in your own teaching.
Computationally Intensive Methods in Teaching Introductory Statistics
A two-and-a-half day workshop preceding USCOTS taught by Webster West (Texas A&M University) and Roger Woodard (North Carolina State University)
Dates: Tuesday morning through Thursday morning, May 17 to May 19.
Supported by NSF DUE # 0817262 & 0817397
Lodging: Reimbursement for up to 2 nights lodging will be reimbursed following the workshop through a reimbursement process with Texas A&M.
Abstract: This two and one half day workshop will feature materials developed under an NSF funded project to increase the use of modern computationally intensive methods in introductory statistics courses. This workshop will provide an overview of methods such as randomization tests that can be used in introductory courses. This session will also explain the motivations and pedagogical advantages of teaching these modern methods. This workshop will showcase materials developed by the presenters that allow instructors to quickly implement these materials in their own classroom. The workshop will also be participant centered and will help participants develop their own materials to use after the workshop. Participants will be supported after the workshop by an online community hosted by the presenters.
Teaching the Big Ideas in Introductory Statistics
A 2-day workshop preceding USCOTS taught by Deborah Rumsey (The Ohio State University) and Marjorie Bond (Monmouth College).
Supported by NSF DUE # 0942924 & 0942456
Dates: Tuesday afternoon through Thursday morning, May 17 to May 19.
Lodging: One nights lodging will be paid directly to the Embassy Suites hotel for those workshop attendees who need lodging.
Abstract: This workshop features the work of the NSF funded SCHEMATYC project (Statistical Content Helping to Empower Mathematicians at Two-Year Colleges) geared towards instructors who teach statistics at two-year colleges. The program will highlight the statistical concepts that introductory statistics students find the most difficult. It will guide the participants through activities aligned with the Guidelines for Assessment and Instruction in Statistics Education (GAISE) that they can use in their own classroom to cover these topics.
Facilitating Student Projects in Elementary Statistics
A 2-day workshop preceding USCOTS taught by Brad Bailey & Sherry Hix (North Georgia College & State University)
Supported by NSF DUE # 1021584
Dates: Tuesday afternoon through Thursday morning, May 17 to May 19.
Lodging: Workshop participants are responsible for their own lodging.
Abstract: Research suggests that having students complete statistics projects which entail identifying a research question, collecting and analyzing the necessary data and interpreting the results lead to deeper student understanding of statistics and fuller appreciation for the usefulness of statistics. Successful such student projects encompass a number of key tasks that students must carry out with guidance from their instructor. These tasks include defining appropriate variables, constructs, and research questions; locating authentic data; designing and implementing a sampling strategy; collecting the data; organizing and analyzing the data; and interpreting and presenting the results. Participants will have the opportunity to become more effective project facilitators by carrying out these key tasks in accelerated projects, using both t-tests and linear regression as contexts for the projects. In addition to providing participant hands-on experience with each of the project tasks, we will review methods for guiding students through these tasks. Finally, we will focus on details of facilitating the overall project, including project phases and organization, assessment methods, and best practices for implementation.
