Kevin Robinson, Millersville University of Pennsylvania
Tuesday, July 26, 2011 - 2:30pm ET
This webinar will present a simple activity/handout called happyville, a community of 100 households, that has been used successfully in statistics courses. Happyville is utilized throughout the course to aid student understanding of statistical concepts including descriptive statistics, sampling techniques, sampling variation, sampling distributions, central limit theorem, confidence level, confidence intervals and type I & II errors. The happyville activity has the beneficial properties of being used throughout the course, visual demonstration and student engagement. The activity lends itself to both hands on simulation as well as computer based simulation. The activity maintains the attention and engagement of students, enables the students to discover important statistical ideas and overcome misconceptions often encountered in introductory statistics courses.
John Gabrosek, Grand Valley State University
Tuesday, July 12, 2011 - 2:00pm ET
The Journal of Statistics Education (JSE) is a leading journal for the dissemination of knowledge for the improvement of statistics education at all levels, including elementary, secondary, post-secondary, post-graduate, continuing, and workplace education. Current JSE Editor John Gabrosek will discuss how JSE handles submissions. Discussion will include guidelines and tips for writing papers for JSE and for reviewing papers as a JSE referee or Associate Editor.
John McKenzie, Babson College
Tuesday, June 28, 2011 - 2:30pm ET
This webinar explains how crossword puzzles can be used as in-class exercises, quizzes, and examination questions in applied statistics courses to assist the students in learning basic statistical terminology. It presents innovative numerical crossword puzzles that can be to ask questions about statistical software output. It explains how the use of such puzzles was impractical in the past due to time it took to construct them but that this is no longer the case with the availability of a number of Internet sites.
Rebekah Isaak, Laura Le, Laura Ziegler, and the CATALST Team
Tuesday, June 14, 2011 - 2:00pm ET
This webinar provides an overview of the research foundations of a radically different introductory statistics course: the CATALST course. This course teaches students the skills they need in order to truly cook with statistics, not just the procedures they need in order to follow a statistical recipe. In addition to the research foundations of the course, we will describe unique aspects of this course as well as details of a one-year teaching experiment to learn how this course can be taught and its impact on student learning.
David Lane, Rice University
Tuesday, May 24, 2011 - 2:30pm ET
The concept of central tendency is typically taught by presenting measures of central tendency and then describing their properties. A (perhaps) better alternative is to think about different ways in which central tendency can be defined and then find statistics that fit these definitions. An activity using Java applets that allows students to discover statistics for each of three definitions of central tendency will be presented.
Jerry Moreno, John Carroll University
Tuesday, May 10, 2011 - 2:00pm ET
Forty-three states have signed on to the mathematics part of the Common Core State Standards (CC). Statistics and Probability play a prominent part in CC grades 6-11 for all students. How may Stats 101 have to change to accommodate potentially better prepared quantitatively literate students?
Susan Holmes & Nelson Ray, Stanford University
Tuesday, April 26, 2011 - 12:00pm ET
We will discuss how we coordinated, held, and judged a wine pricing competition (hosted on Kaggle-in-Class - inclass.kaggle.com) to engage students in applying prediction techniques learned in our data mining class at Stanford. We found that with proper incentives, the competition was very successful in getting students interested in working collaboratively in a race against the clock to eke out additional predictive performance in their models.
Tuesday, April 12, 2011 - 2:00pm ET
The role that data collection plays in causal inference is of fundamental importance in introductory statistics, and yet is outside the comfort zone for many of us. In this webinar, I'll discuss why causal inference is important and also fun, and give some advice for teaching this topic.
Nicholas Horton, Smith College
Tuesday, March 22, 2011 - 12:00pm ET
A challenge in introductory statistics is to motivate the estimation of unknown population parameters. In this activity, we allow students to estimate the proportion of the continental United States that is within a mile of a road by repeatedly sampling latitudes and longitudes and viewing that location using an internet mapping service. Technology is used to generate random values within a specified geographic rectangle, which populate a data collection spreadsheet. Students are instructed how to use MapQuest.com to determine if the random location is within the continental US and if so, whether it is within a mile of a road. This data collection task helps to fix ideas of study design ("what if the point lands in the middle of one of the Great Lakes"?) as well as motivate the estimation of an unknown proportion. Individual confidence intervals can be created and compared, as well as creation of a class-wide confidence interval. This activity can be used in introductory classes at all levels.
Cliff Konold, Director, Scientific Reasoning Research Institute, University of Massachusetts Amherst
Tuesday, March 8, 2011 - 2:00pm ET
Generally in learning statistical inference, students reason backwards from data to the (usually invisible) process that produced them. I will demonstrate an alternative approach in which students begin at the process end, designing their own "data factories." Based on their output, students modify their factories such that, for example, a collection of cats produced by a cat factory has features that look more like real cats. This work is part of the NSF-funded "Model Chance" project. In this project, we have been adding probability modeling to the existing data-visualization capabilities of TinkerPlots and, using that environment, exploring how data and chance might be better integrated in our instruction beginning in the middle school.