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Constantly Improving Introductory Statistics:
The Role of Technology
Thursday, July 24, 2003
8:00 a.m. Registration 4th Floor Atrium, North End
Breakfast (juice, coffee, fruit, bagels, muffins) 5th Floor Atrium, South End
[Thanks to Duxbury Press]
9:00 a.m. Welcome and Introductions Room 4151
Opening Comments and Introductions David McNitt
Welcome Janet Glocker, Vise President, Academic Services, MCC
Announcements Patricia Kuby
9:30 a.m. Session 1 Opening Keynote Address Room 4151
S.1 Paul F. Velleman, Cornell University Host: David McNitt
Carving the Introductory Statistics Course
"I saw the angel in the marble and carved until I set it free."-- Michelangelo
Advanced classes are defined by what they include, but introductory classes are defined by what they exclude. An introductory course that tries to tell the whole truth would be far too narrow to be useful or honest. But too often we concentrate on listing all the things we want to cover and try to motivate students by starting with an overwhelming overview of the topic. That can convince students that their introductory Statistics class isn't about to be their favorite.
There is an interesting, subtle, beautiful introductory statistics course hiding in there, but it is often obscured by topics, notation, terminology, and tedium that don't belong. Even the core topics can be confused by the choice of presentation order. I propose to sculpt the introductory statistics course by carving away the parts that are not needed and then polishing what's left behind into a simple presentation order so that we can set free the angel.
[Refreshments in 5th Floor Atrium]
10:45 a.m. Session 2 Directed Workshops
S.2.1 Robert H. Carver, Stonehill College Host: Karen Wagner Room 5008
"Using Computational Software to teach Statistical Concepts"
This session will engage participants in the use of standard computational software (like Minitab or SPSS) to develop conceptual understanding among introductory students. Because such software spares both students and faculty from the computational burden associated with many techniques, we are free to exploit the software and available classroom and homework time to invite students to investigate underlying theory and to reason about their results. The workshop will begin with some interactive demonstrations of the approach, focusing on basic concepts such as variation, the shape of a distribution, random sampling, the fundamental logic of hypothesis testing, and linear (vs. non-linear) relationships. Then participants will begin to develop exercises for topics and concepts of their own choice. Participants should have some familiarity with Minitab and are welcome to bring a favorite dataset. They should bring an answer to this question: "What statistical concept do your students have the greatest difficulty mastering?" [Target Audience: Any instructor who has computational software available in the classroom.]
S.2.2 Floyd Bullard, NC School of Science and Mathematics Host: Robert Nenno Room 4035
The German Tank Problem
This is a classic problem that has been used by high school and college teachers for years to introduce students to the idea of sampling variability. It is inherently engaging, it is based on a real historical situation, it requires students to think creatively and logically, and it introduces not only sampling variability but also the ideas of bias, low variability, and robustness, and how we judge sample statistics. For all of these reasons it endures; this session is intended for teachers who have are not yet familiar with this superb problem. [Target Audience: All]
S.2.3 John D. McKenzie, Jr., Babson College Host: Pam Keyes Room 4037
Criteria for Selecting Data Sets for an Introductory Applied Statistics Course
The first criterion for deciding upon a data set is that it illustrates a specific technique or concept. Two other common criteria are whether it will be interesting to students and if it comes from the "real world". But many other criteria should be considered when one selects the data sets for a first course in order to prepare students for their future statistical work. With todays software there should be data sets that include alphanumeric qualitative (categorical) variables as well as numeric qualitative variables. In addition to cross-sectional data sets, there should be time-series data sets. Some data sets should illustrate stacked data, while others should illustrate non-stacked data. There should be data sets that are much larger than the typical sets included with most textbooks. And, some data sets should include missing data (which is rarely presented in textbooks but always seen in practice) and possibly include data that must be cleansed (which is 80% of the work of many statistical projects). This session will present examples of data sets that illustrate each of these and other criteria. Members of the audience will be asked to put forward their favorite data sets and to explain why they decided to use them. [Target Audience: Any instructor who selects data sets for his or her statistical software in the classroom.]
12:15 p.m. Lunch 5th Floor Atrium, South End
1:15 p.m. Session 3 Directed Workshops
S.3.1 Webster West, University of South Carolina Host: Kimberley Martello Room 5008
Interactive tools for teaching interactive statistics
A survey of the large number of interactive Java applets that are currently available for statistics education will be provided. Focus will be placed on the methods used to achieve pedagogical goals. Choosing the proper strategy for introducing applets in a statistics course will be discussed, and the technical details on how to get started using applets will also be covered. [Target Audience: All]
S.3.2 Ginger Holmes Rowell, Middle Tennessee State U Host: Jason Mahar Room 5007
Finding the Right Web-Materials to use in Your Class: On the Road to a Statistics Digital Library
A large quantity of materials for teaching statistics is available on the web. These web materials include on-line textbooks, complete lesson plans, applets, statistical calculators, and more. One difficulty can be finding the right materials for your specific classroom activity. In this session, we will examine large collections of on-line interactive materials for teaching introductory statistics. Additionally, we will discuss the qualities of the materials that make them effective for student learning. One way to help users find the right web-based statistics materials for their specific goal is to have a good library classification system. This library of web-materials must of course be a digital library. We will examine existing digital libraries that contain undergraduate statistics materials and discuss the plan for developing an undergraduate statistics digital library. [Target Audience: All]
S.3.3 Joe Gallo, Ferronics, Inc. Host: Peter Collinge Room 4153
Using Elementary Statistical Tools as an Adjunct to Decision Making in an Industrial Setting
Problem solving and decision-making can benefit from the application of statistics and statistical thinking. However, data and problems encountered in industry can be very different from textbook problems. Often the information is incomplete, the data is dirty, and the client lacks statistical sophistication. I'll present a number of brief case studies where elementary statistical tools were used to investigate problems and arrive at reasonable solutions. [Target Audience: anyone interested in the challenges presented by real world situations]
[Refreshments in Hallway outside Room 4151]
2:30 p.m. Session 4 Address Room 4151
S.4 Michael D. Larsen, University of Chicago Host: Bob Johnson
Using Technology to Connect Topics and Contrast Scenarios
Technology, including graphing calculators, computer statistical packages, and Internet pages and applications, can be used to connect topics that appear in different sections of introductory statistics and probability textbooks. They also can be used to quickly contrast various scenarios and their impact on statistical methods and probability calculations. Courses have syllabi and books have outlines that guide the presentation of material. This organization is very important and helpful to students. The application of statistics to research questions, however, involves relationships among numerical and graphical descriptive statistics, probability models, and inferential methods. A few ideas for connecting topics and for creating enlightening contrasts using technology are presented.
3:45 p.m. Session 5 Discussion Sessions
S.5.1 Floyd Bullard, NC School of Science and Mathematics Room 4035
AP Statistics Sharing Session
This somewhat unstructured hour is intended for new and experienced teachers of AP statistics to share or ask questions about anything concerning the AP statistics curriculum or exam. Experienced teachers are encouraged to bring their ideas about how to teach particular topics in the curriculum--preferably approaches that go "beyond the formula" and engage students particularly well with concepts. New teachers are encouraged to bring their questions. College professors curious about the curriculum or exam are of course welcome also. The discussion moderator, Floyd Bullard, has taught the AP statistics curriculum for four years and has been an AP statistics exam reader for two years.
S.5.2 Robert H. Carver, Stonehill College Room 4057
Sharing Session: Collegiate Concerns
This session is designed as an open forum for participants who teach at the college level. In an informal and loosely-structured format, we seek to exchange ideas about what works and what doesn't in the Introductory Statistics course. Participants should be prepared to share common concerns, frustrations, and transferable stories of success and failure. Particularly useful would be examples of good ideas that had unintended consequences (e.g. an apparently promising dataset that did not pan out). This is not a place to air provincial quibbles about home institutions or simply trade war stories, but an opportunity to learn from the experience of others and to advance our teaching.
S.5.3 Open Lab Available for those wishing to use computers to practice Room 5007
S.5.4 Open Lab new skills, complete workshop projects, or use the Internet. Room 5008
3:30 p.m. Book Exhibit 4th Floor Atrium, South Side
4:00 p.m. Reception 4th Floor Atrium & Room 4013
6:00 p.m. Dinner 5th Floor Atrium, South End
7:00 p.m. Session 6 After Dinner Address 5th Floor Atrium, South End
S.6 Roger Hoerl, GE Global Research Host: Michael Allen
Academic Statistics Education: What is the Business World Really Looking For?
There has been a great deal of discussion in the statistical literature about how to make improvements to statistics education, particularly in introductory courses. This is certainly a positive and much needed development, but begs the question of what specifically we are trying to accomplish in the statistics education of non-statisticians? Without agreement on fundamental objectives, it will be impossible to ever obtain consensus on the necessary content or pedagogy improvements. This session will address this underlying issue of appropriate objectives for statistics education, especially introductory courses, as it pertains to non-statisticians who will find employment in business and industry. It will suggest that most introductory statistics courses offered today do not appear to be based on a set of clear objectives of value to business, and will benchmark other disciplines, such as psychology, physical sciences, and so on, to contrast their approach to this issue. It will be argued that understanding of a core set of statistical concepts is most critical to develop in students, as opposed to memorization of formulas, or even technical skills in specific statistical methods. This is what is most desired and valued by private sector employers.
Friday, July 25, 2003
8:00 a.m. Breakfast (juice, coffee, fruit, bagels, danish) 5th Floor Atrium, South End
8:40 a.m. Announcements Michael Wagner Room 4035
Announcements Kimberley Martello Room 5007
Announcements Mark Harris Room 5008
8:45 a.m. Session 7 Directed Workshops
S.7.1 Floyd Bullard, NC School of Science and Mathematics Host: Michael Wagner Room 4035
The Subjectivity of Hypothesis Tests
Many people view statistics as a bastion of objectivity, but that is not the case. We make subjective choices every time we do statistical analysis, and those choices often have an effect on the conclusions we draw. In this session, data sets (real and fictitious) will be presented that demonstrate the dependence of the p-value of a hypothesis test on the method of analysis chosen. [Target Audience: All]
S.7.2 Michael D. Larsen, University of Chicago Host: Kimberley Martello Room 5007
Internet Sites and Their Use in Introductory Statistics
Several Internet sites are available for teaching and learning statistics. Many sites provide raw data, graphs and numerical summaries of data, descriptions of experiments, surveys, and other studies, and tools for simulation. Using Internet material in introductory statistics is still challenging because the material varies widely in quality and content and usually is not organized for the purpose of learning and teaching statistics. Rarely is material tied to an outline and accompanied by study questions and writing assignments. Examples will be presented that illustrate the selection and use of Internet sites in introductory courses. Selection criteria such as data quality, generalizability, reproducibility, and ease of use will be discussed. Ideas for incorporation of Internet material in class room activities, in computer labs, and in homework assignments will be considered. Teacher participation in the session by way of sharing ideas and experience with Internet sites will be encouraged. [Target audience: All]
S.7.3 Webster West, University of South Carolina Host: Mark Harris Room 5008
WebStat: A tool for educational data analysis
WebStat is a freely available Web-based data analysis package. The pedagogical goals of the package will be discussed, and a complete demonstration of WebStats capabilities for enhancing the teaching of statistics will be provided. The technical aspects of using WebStat as the primary data analysis tool for a statistics course will also be considered. [Target Audience: All]
[Refreshments in 5th Floor Atrium]
10:00 a.m. Session 8 Directed Workshops
S.8.1 Sterling Hilton, Brigham Young University Host: Patricia Kuby Room 4035
Using technology to teach variability
A primary objective in our introductory statistics course is to help students understand the concept of variability and its relationship (via the concepts of sampling variability and sampling distributions) to statistical inference. We have developed Flash lessons using animation, simulation and applets to help teach these concepts. In this workshop, we will demonstrate the lessons that teach the concepts of standard deviation and sampling distributions. We will also discuss some strategies for using these lessons and some ideas for assessment. [Target Audience: All]
S.8.2 Roger Hoerl, GE Global Research Host: Jason Mahar Room 4036
Six Sigma: Why Should Academia Care, and How Should it Respond?
Six Sigma is having a profound impact on the application of statistics in business and industry, and will likely have similar impacts on government and biological use of statistics in the future. GE alone has trained hundreds of thousands of people in statistical methods through its Six Sigma initiative, perhaps making this initiative the largest statistical education effort in history. There have been a number of challenges to integration of Six Sigma into academic curricula that have slowed academia's response, however:
Difficulty in cutting through the media "hype" to understand what Six Sigma is technically.
Deciding how Six Sigma best fits in.
Finding something to delete from an already overcrowded course syllabus.
Finding appropriate materials and case studies.
Belief on the part of instructors that Six Sigma is a temporary "fad", not "scientific", or only applicable to industrial applications.
This talk will explain what Six Sigma is, comment on the educational relevance and value of Six Sigma, and then suggest several specific ways that it can be integrated into academic curricula. These suggestions will address each of the challenges listed above and will be illustrated using a case study from a Six Sigma course at Virginia Tech. [Target Audience: All]
S.8.3 John D. McKenzie, Jr., Babson College Host: Robert Nenno Room 4037
Using Microsoft Excel for Applied Statistics Courses
In recent years Microsoft Excels statistical functions and Analysis ToolPak have been increasingly used in the introductory applied statistics course. Yet, the American Statistical Association has recommended that it is "not sufficient for the teaching of statistics, let alone for research and consulting". This session will present an up-to-date overview of why one should or should not use this spreadsheet for K-16 applied statistics courses. It will expand upon the session at 2001 Joint Statistical Meetings on the use of Microsoft Excel for statistical analyses, organized by Jon Cryer and the speaker. At that session two of the speakers presented a large number of concerns about using this popular spreadsheet package in either the classroom or the workplace. Some of these concerns were computational; others were related to documentation and ease of use. Many users of Excel are often unaware of some of these concerns. Another panel member who is employed by Microsoft acknowledged that there were serious problems with its use for statistics. He hoped that the most severe problems would be eliminated in future, but not immediate, releases. This session will identify the strengths and weaknesses in using Excel for each component of the first course: descriptive statistics (graphical displays and summary measures), elementary probability, introductory inferential statistics, and common statistical analyses such as regression. It will also present alternatives to using this spreadsheet in the classroom, such as Excel add-ins, Internet freeware, and student versions of statistical software. The session is planned so that there will be ample opportunity for audience participation. [Target Audience: Any instructor who uses, or plans to use, computational software in the classroom.]
11:30 a.m. Lunch 5th Floor Atrium, South End
12:30 p.m. Session 9 Panel Discussion
S.9 R. Carver, S. Hilton, G. Rowell, W. West Host: Bob Johnson Room 4151
Our Toolbox is Overflowing: Towards a Framework for Mapping Technologies and Topics in Introductory Statistics
The past fifteen years have been a period of great creativity and invention in statistics education. We have radically rethought our aims and have generated an impressive array of new tools for teaching and for doing statistics. Our toolbox overflows with reliable fine old tools and gleaming new power tools. The availability of new teaching technologies should influence the content of our courses, and the content of our courses should influence our choice of technologies.
In this panel discussion, we will sketch a framework for matching topics and technologies. Among other issues, we will attempt to: classify the available technologies (ranging from chalk and manipulatives to applets, analytical software, and conceptual software); to consider criteria for selecting a given technology to teach a given topic; to inquire into the characteristics of an effective teaching technology or tool; to ask how our tools can be used to promote conceptual understanding; to examine the role of these tools in teaching an on-line introductory statistics course; and to consider strategies that we can use to assess the impact of available technologies.
[Refreshments in Hallway outside Room 4151]
1:45 p.m. Session 10 Closing Keynote Address Room 4151
S.10 Paul F. Velleman, Cornell University Host: David McNitt
Teaching Statistical Thinking in Four Unnatural Acts
Statistics is the art of learning about the world from data. But it is always a challenge to teach an art. There's a way of thinking about the world, about the data, and about how they relate to each other that informs good statistics. But teaching how to think statistically is as difficult as teaching how to write well or how to draw a draped figure. We will discuss four habits of mind that form a core of statistical thinking:
Critical Thinking,
Skepticism,
A Focus on our own Ignorance, and
Attention to Counterintuitive Chance Events.
Each plays an important role in statistical thinking, but each is unnatural for humans. When we identify these habits of mind and acknowledge to our students that they are right to find them unnatural, it becomes easier to understand and to learn them.
2:45 p.m. Wrap-up Room 4151
Completion and Collection of Evaluation Forms
Awarding of Door Prizes
Have a safe trip home!
PROGRAM
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