Alison Gibbs & Emery Goossens, University of Toronto
Tuesday, March 18, 2014 - 12:00pm
Our current students are among the first to have been vaccinated against HPV. Have they ever considered how the accumulation of the evidence for the efficacy of the vaccine resulted in recommendations for its widespread provision? Using data sourced from a meta-analysis of clinical trials for HPV vaccines, we will examine this evidence. We will show how these data can be used to illustrate applications of methods of categorical data analysis, for students in courses at a variety of levels. And we will describe how this case study can be used to promote discussion of concepts in the design of experiments, statistical concerns such as independence of observations, and the importance of context in the interpretation of the results of data analyses.
Aimee Schwab & Erin Blankenship; University of Nebraska, Lincoln
Tuesday, March 11, 2014 - 4:00pm
Like many other research universities, the University of Nebraska-Lincoln relies on graduate student instructors to cover a large portion of the instructional load in the introductory course. In order to better prepare new graduate student instructors, we have implemented a mentoring program that pairs new GTAs with experienced graduate student instructors. Through the mentoring program, the new GTA has a semester to acclimate to graduate school and their new role as instructor, and the senior GTA has the opportunity to emerge as a teacher leader.
Michael Bulmer, University of Queensland
Tuesday, February 25, 2014 - 3:00pm
The Island (island.maths.uq.edu.au) is an online virtual population to support learning and teaching in experimental design, epidemiology and statistical reasoning. This month the Island is celebrating five years since the switch was flicked and the population came to life. In this presentation we will give some examples of how the Island has been used in those five years and suggest some activities for you to try.
Brad Bailey & Dianna Spence, University of North Georgia
Tuesday, February 18, 2014 - 12:00pm
In this presentation we will discuss student-directed discovery projects in statistics, which are intended as the means through which statistical content is taught to the students. In particular, we will delineate the purpose and scope of a project covering linear regression analysis and another project covering comparisons with basic t-tests. We will describe curriculum materials developed to help instructors facilitate such projects and provide the web address where these materials can be accessed. We will give examples of how instructors use the curriculum materials to guide students through the projects' stages. In particular, the materials can be used to provide the students with clear information about the project requirements and what activities the students are expected to be engaged in during each phase of the student-directed projects. These projects are truly student-directed in that the students select their own research topic, define their own variables, and devise and carry out their own data collection plan before analyzing and interpreting their data. The students report their results in two forms: a written report is provided to the instructor and a brief formal presentation is made before the rest of the class. In both formats, the students report the findings of their research project, as well as explain why they chose the particular research topic, explain how they gathered, organized and analyzed the data and list any short-comings they perceive in their own project. Our presentation will include specific examples of projects that students have conducted. Finally, we will also discuss the rationale for assigning such projects, including the potential benefits of such projects - benefits suggested by both prior and on-going research - and possible factors mediating those benefits.
Audbjorg Bjornsdottir, University of Minnesota
Tuesday, February 11, 2014 - 12:00pm
This presentation will be about collaborative tests, where students are allowed to work together during the exam. It will include a review about the effectiveness and different formats of collaborative tests along with successful strategies for implementing them in face-to-face and online statistics classes.
Jennifer L. Green, Montana State University and Erin E. Blankenship, University of Nebraska-Lincoln
Tuesday, January 21, 2014 - 3:30pm
We developed an introductory statistics course for pre-service elementary teachers. In this webinar, we will describe the goals and structure of the course, as well as the assessments we implemented. Overall, the course aims to help pre-service teachers recognize the importance of statistics in the elementary curriculum, as well as the integral role they, as teachers, can play in a student's entire statistical education.
A. John Bailer, Miami University
Tuesday, January 14, 2014 - 12:00pm
The need for a larger proportion of the workforce to enter well equipped with mathematics and statistics skills has been acknowledged in a number of recent reports. To address this need, action must be taken by all stakeholders involved in preparing students for 21st century workforce demands. A collaboration of mathematics and statistics professional societies recently culminated in a workshop focused on identifying strategic steps that might be taken to dramatically increase the flow of mathematical sciences professionals into the workforce pipeline.
Amy G. Froelich & Dan Nettleton, Iowa State University
Tuesday, November 19, 2013 - 12:00pm
Many new parents have heard claims of a striking resemblance between them and their babies. As parents ourselves, we were skeptical of such claims so we devised a study to objectively answer the question "Does my baby really look like me?" In this webinar, we will present a study to test whether neutral observers perceive a resemblance between a parent and a child. We will demonstrate the general approach with two parent/child pairs (Amy and her daughter and Dan and his son) using survey data collected from introductory statistics students serving as neutral observers. We will then present ideas for incorporating the study design process, data collection, and analysis into different statistics courses.
M. Ryan Haley, University of Wisconsin Oshkosh
Monday, November 18, 2013 - 12:00pm
This paper describes a textbook -development paradigm that has the flexibility to meet the specific needs of a department, college, and surrounding business community, while simultaneously lowering costs for students, facilitating the transition from intro-level to mid- and upper-level courses, preserving professor-specific preferences over course content and structure, increasing the quality and uniformity of the curriculum, overcoming difficulties of traditional rental programs, enhancing the professional development and teaching ability of professors, and improving student learning outcomes.
Nicholas J. Horton, Amherst College
Tuesday, November 12, 2013 - 12:00pm
Undergraduate study of statistics has been growing in recent years, with the number of students completing stats majors in the United States doubling in the past 5 years. At the same time, the amount and complexity of data being collected increases almost without bound. What should students completing undergraduate majors, minors or concentrations in statistics learn in order to help analyze this flood of information? The American Statistical Association endorsed guidelines in this area in 2000, and a workgroup is now considering what needs to be changed and amplified from the earlier report and supporting materials. In this webinar, participants will hear more about the process, learn about and identify key issues to be considered, and have the opportunity to make suggestions about areas and topics to explore.