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.
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.
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.
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.
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.
Paul Roback, St. Olaf College
Tuesday, June 22, 2010 - 2:30pm
This webinar will describe an in-class activity, motivated by Case Study 1.1.1 in The Statistical Sleuth, in which students compose haiku poems about statistics. Their poems are used to introduce two-sample t-tests and randomization tests. In addition, the in-class experiment leads to good discussion about experimental design issues, where students compare our design to the actual experiment described in Amabile et al.(1985; "Motivation and Creativity: Effects of Motivational Orientation on Creative Writers", Journal of Personality and Social Psychology 48(2): 393-399). I use this activity on the first day of our second course in applied statistics (Statistical Modeling), but it could easily be used in an introductory course as well.
Examples of haiku poems which have resulted from this activity can be found at www.causeweb.org/cwis/SPT--FullRecord.php?ResourceId=1883.
Lynette Hoelter, University of Michigan
Tuesday, June 8, 2010 - 2:00pm
This webinar will introduce several sources of data and tools that could be useful in both general and social science-specific statistics instruction. The Social Science Data Analysis Network (SSDAN) and the Inter-university Consortium for Political and Social Research (ICPSR), both a part of the University of Michigan's Institute for Social Research, are collaborating on two NSF-funded projects to support quantitative literacy in the social sciences. Resources from each organization and TeachingWithData.org, a result of the partnership, will be highlighted. Materials range from small extracts of data from the Census and American Community Surveys used with specific teaching modules to full datasets with accompanying online analysis tools.
Ivan Ramler, St. Lawrence University
Tuesday, May 25, 2010 - 2:30pm
This webinar will discuss an undergraduate Mathematical Statistics course project based on the popular video game Guitar Hero. The project included:
Developing an estimator to address the research objective "Are notes missed at random?"
Learning bootstrapping techniques and R programming skills to conduct hypothesis tests and
Evaluating the quality of the estimator(s) under certain sets of scenarios.
Tuesday, May 11, 2010 - 2:00pm
This webinar will present data, tools, materials and the pedagogical approach of the Statistics Online Computational Resource (SOCR) for technology-enhanced probability and statistics education. Following a review of the different types of SOCR online resources, we will go over two specific classroom utilization examples. The first one provides a hands-on demonstration of a statistical concept (CLT) using interactive virtual experiments and simulations. The second example will showcase the use of SOCR resources to address interesting social, health, environmental, scientific, and engineering challenges. In this case, we'll focus on the Ozone pollution in California, formulate health-related hypotheses, identify appropriate data and employ web-based exploratory and statistical data analysis tools.
What is www.SOCR.ucla.edu?
The Statistics Online Computational Resource provides portable online aids for probability and statistics education, technology based instruction and statistical computing. SOCR tools and resources include a repository of interactive applets, computational and graphing tools, instructional and course materials.
SOCR aims to develop new Java applets, design diverse extensible SOCR learning activities, develop XML/HTML navigation/search tools for interactive materials, and validate and assess technology-enhances pedagogical techniques.
Tools/Applets: Distributions, Experiments, Analyses, Games, Modeler & Graphs.
Multilingual instructional resources: EBooks, continuing statistics education workshops/seminars
Learning activities: interactive, data-driven and technology-enhanced learning activities
Central Limit Theorem
Hands-on California Ozone Data Activity
Data: Diverse publicly accessible datasets for copy-paste/download utilization
Example: Latin Letters Frequency Distribution
Dissemination: papers, conferences, workshops, etc.
SOCR Evaluation and Efficacy
We have conducted several control-based studies of the efficacy of technology-enhanced statistics education. Using IRB-approved studies, quantitative and qualitative measures of student performance were recorded in classes using traditional (control) instruction (R or Stata based) and classes using SOCR resources and tools. Non-parametric analyses of the data showed very statistically significant (SOCR) treatment effects (p < 10-4) on student performance and perception of the material. The practical significance of these treatment effects were more modulated. More details about these studies are available here.
Main SOCR server, applets
Data, activities and EBooks
Feedback and Forum
Graphical SOCR Navigator
Shonda Kuiper, Grinnell College
Tuesday, April 27, 2010 - 2:30pm
Educational games have had varied success in the past. However, what it means to incorporate games into the classroom has changed dramatically in the last 10 years. The goals of our games are to 1) foster a sense of engagement, 2) have a low threat of failure, 3) allow instructors to create simplified models of the world around us, and 4) motivate students to learn. This webinar will use the same reaction time game to demonstrate a simple 1- 2 day activity that is appropriate for introductory courses as well as an advanced project that encourages students to experience data analysis as it is actually practiced in multiple disciplines. In the introductory activity students are asked to spend 15 minutes playing an on-line game. Data collected from the game is used to demonstrate the importance of proper data collection and appropriate statistical analysis. The advanced project asks students to read primary literature, plan and carry out game based experiments, and present their results.