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  • Developing a Statistics Teaching and Beliefs Survey

    Jiyoon Park & Audbjorg Bjornsdottir, University of Minnesota
    Tuesday, November 9, 2010 - 2:00pm
    This webinar presents the development of a new instrument designed to assess the practices and beliefs of teachers of introductory statistics courses. The Statistics Teaching Inventory (STI) was developed to be used as a national survey to assess changes in teaching over time as well as for use in evaluating professional development activities. We will describe the instrument and the validation process, and invite comments and suggestions about its content and potential use in research and evaluation studies.
  • Using Your Hair To Understand Descriptive Statistics

    Tisha Hooks, Winona State University
    Tuesday, October 26, 2010 - 2:30pm
    The purpose of this webinar is to introduce an activity to enhance students' understanding of various descriptive measures. In particular, by completing this hands-on activity students will experience a visual interpretation of a mean, median, outlier, and the concept of distance-to-mean.
  • Using Calibrated Peer Review in Statistics and Biology: A Coordinated Statistical Literacy Project

    Ellen Gundlach & Nancy Pelaez, Purdue University
    Wednesday, October 13, 2010 - 2:00pm
    Ellen and Nancy use Calibrated Peer Review, an online writing and peer evaluation program available from UCLA, to introduce statistical literacy to Nancy's freshman biology students and to bring a real-world context to statistical concepts for Ellen's introductory statistics classes in an NSF-funded project. CPR allows instructors in large classes to give their students frequent writing assignments without a heavy grading burden. Ellen and Nancy have their students read research journal articles on interesting subjects and use guiding questions to evaluate these articles for statistical content, experimental design features, and ethical concerns.
  • Linear Statistical Models As A First Statistics Course For Math Majors

    George Cobb, Mount Holyoke College
    Tuesday, October 12, 2010 - 2:00pm
    What's the best way to introduce students of mathematics to statistics? Tradition offers two main choices: a variant of the standard "Stat 101" course, or some version of the two-semester sequence in probability and mathematical statistics. I hope to convince participants to think seriously about a third option: the theory and applications of linear models as a first statistics course for sophomore math majors. Rather than subject you to a half-hour polemic, however, I plan to talk concretely about multiple regression models and methodological challenges that arise in connection with AAUP data relating faculty salaries to the percentage of women faculty, and to present also a short geometric proof of the Gauss-Markov Theorem.
  • Why Not Just Take A Census?

    Carolyn Cuff, Westminster College
    Tuesday, September 28, 2010 - 2:30pm
    Students must confront their misconceptions before we can teach them new concepts. Naively, a census is an accurate method to quantify a population parameter. A very brief, memorable and easy to implement activity demonstrates that a census is at best difficult even for a small and easily enumerated population. Exercise Documentation
  • Using baboon "mothering" behavior to teach Permutation tests

    Thomas Moore, Grinnell College
    Tuesday, September 14, 2010 - 2:00pm
    Permutation tests and randomization tests were introduced almost a century ago, well before inexpensive, high-speed computing made them feasible to use. Fisher and Pitman showed the two-sample t-test could approximate the permutation test in a two independent groups experiment. Today many statistics educators are returning to the permutation test as a more intuitive way to teach hypothesis testing. In this presentation, I will show an interesting teaching example about primate behavior that illustrates how simple permutation tests are to use, even with a messier data set that admits of no obvious and easy-to-compute approximation.
  • Using the Jigsaw Method for Exam Reviews in the Introductory Statistics Classroom

    Jackie Miller, The Ohio State University
    Tuesday, August 24, 2010 - 2:30pm
    When I took a graduate course in College Teaching, I learned the jigsaw method. The jigsaw is a cooperative learning technique where students work together in a "home" group on a specific task and then are placed into "jigsaw" groups made up of one member from each home group. For example, if there are 25 students in the class, 5 students would be assigned to each of the A, B, C, D, E home groups, and each jigsaw group would each one member from A, B, C, D, and E. While in the jigsaw groups, the students teach each other what they learned in their home groups. I recall bringing the idea back with me to our elementary statistics course where it has been used successfully since 1996. Recently a graduate teaching assistant (GTA) suggested to other GTAs that this might be good in our introductory statistics course, and the activity has been adopted successfully . As structured, the jigsaw can be used in an exam review in statistics by assigning students to, say, 5 exercises that they need to master before they go to their jigsaw groups to teach others about their exercise. During this webinar, I will present how the jigsaw is done and address questions like: How do you budget your time for this class activity? How do you know that students are teaching the correct answer? How do you know that students are not just furiously writing down answers instead of listening to understand the concept? Can this work for you? By the end of the webinar, hopefully you will be as intrigued as I was to learn about the jigsaw method and will want to try it in your classroom.
  • Helping Students Understand the Meaning of Random: Addressing Lexical Ambiguity

    Diane Fisher, University of Louisiana at Lafayette; Jennifer Kaplan, Michigan State University; and Neal Rogness, Grand Valley State University
    Tuesday, August 10, 2010 - 2:00pm
    Our research shows that half of the students entering a statistics course use the word random colloquially to mean, "haphazard" or "out of the ordinary." Another large subset of students define random as, "selecting without prior knowledge or criteria." At the end of the semester, only 8% of students we studied gave a correct statistical definition for the word random and most students still define random as, "selecting without order or reason." In this session we will present a classroom approach to help students better understand what statisticians mean by random or randomness as well as preliminary results of the affect of this approach.
  • Supporting Statistical Thinking Through a Capstone Project

    Herle McGowan, North Carolina State University
    Tuesday, July 27, 2010 - 2:30pm
    In this webinar, I will discuss the end-of-semester project that is used in North Carolina State's introductory statistics course. This project supports statistical thinking by allowing students to apply knowledge accumulated throughout the semester. Students are presented with a research question and must design and carry out an experiment, analyze the resulting data and form a conclusion over the course of several class periods.
  • Pedagogical simulations with StatCrunch

    Webster West, Texas A&M University
    Tuesday, July 13, 2010 - 2:00pm
    In introductory statistics courses, web-based applets are often used to visually conduct large simulation studies illustrating statistical concepts. However, it is difficult to determine what (if anything) students learn from repeatedly pressing a button when using applets. More advanced options such as writing/running computer code are typically considered to be much too advanced for most introductory courses. The web-based software package, StatCrunch, now offers simulation capabilities that strike a middle ground between these two extremes. The instructor/student needs only to perform a small number of steps using the menu driven interface with each step being key to understanding the underlying data structure. This talk will cover the steps required to study concepts such as the central limit theorem, confidence intervals, hypothesis testing and regression using StatCrunch.