Uri Treisman, Director, Charles Dana Center, University of Texas at Austin
Tuesday, February 8, 2011 - 2:00pm
Developmental education in America's community colleges has been a burial ground for the aspirations of our students seeking to improve their lives through education. Under the leadership for the Carnegie Foundation for the Advancement of Teaching and the Charles A. Dana Center, nineteen community colleges and systems are building accelerated pathways to and through developmental education with the goal of helping students with low levels of mathematical preparation complete a college credit bearing, transferable statistics course within one year. Uri will describe the work to date, the challenges the initiative faces, and the underlying ideas of improvement science that are driving its development.
Randall Pruim, Calvin College
Tuesday, January 25, 2011 - 2:30pm
One challenge in any introductory statistics course is helping our students understand the logic of hypothesis testing. In this webinar I'll demonstrate one of my favorite examples for doing this. The data are a sample of golfballs. The hypothesis is that the number on a golfball is equally like to be a 1, 2, 3, or 4. Using a function written in R, I allow students to design their own test statistics and then produce a graphical display of the sampling distribution and calculate empirical p-values. This activity can be used in introductory classes at all levels - even if you don't cover goodness-of-fit testing. It can be used as a first introduction to inference, as a motivation for the chi-squared test statistic, as an example of goodness of fit testing, or as a demonstration of simulation-based inference.
Tuesday, January 11, 2011 - 2:00pm
Since formal hypothesis testing and inference methods can be a challenging topic for students to tackle, introducing informal inference early in a course is a useful way of helping students understand the concept of a null distribution and how to make decisions about whether to reject it. We will present two computer labs, both using Fathom, that illustrate these concepts using permutation in a setting where students will be answering interesting investigative questions with real data.
Dianna Spence & Brad Bailey, North Georgia College & State University
Tuesday, December 14, 2010 - 2:00pm
When instructors have their students implement "real-world" projects in statistics, a number of questions arise: Where can students locate real data to analyze? What kinds of meaningful research questions can we help students to formulate? What aspects of statistical research can be covered in a project? What are reasonable methods for evaluating the student's work? The presenters will share resources developed during an NSF-funded study to develop and test curriculum materials for student projects in statistics, using linear regression and t-test scenarios.
Brandon Vaughn, University of Texas
Wednesday, December 8, 2010 - 2:00pm
Some students in statistics classes exhibit behaviors that share characteristics with the established construct of learned helplessness. This webinar will discuss this phenomenon, and detail an instrument recently developed which measures this (HILS: Helplessness in Learning Statistics).
Stacey Hancock, Reed College; Jennifer Noll, Portland State University; Sean Simpson, Westchester Community College; and Aaron Weinberg, Ithaca College
Tuesday, November 23, 2010 - 2:30pm
Many instructors ask students to demonstrate the frequentist notion of probability using a simulation early in an intro stats course. Typically, the simulation involves dice or coins, which give equal (and known) probabilities. How about a simulation involving an unknown probability? This webinar discusses an experiment involving rolling (unbalanced) pigs. Since the probabilities are not equal, this experiment will also allow the instructor to have students think about the concept of fairness within games.
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.
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.
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.
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.