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{\*\generator Msftedit 5.41.15.1503;}\viewkind4\uc1\pard\f0\fs22 All Abstracts by speaker\par
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\cf1\b 1\par
William Harkness\b0 , Penn State University\par
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\pard\b0 In the minds of many students, statistics is a subject to be avoided if at all possible. Last year at this conference Paul Velleman, in his closing keynote, said \ldblquote There is an interesting, subtle, beautiful introductory statistics course (out) there, but it is often obscured by topics, notation, terminology, and tedium that don\rquote t belong.\rdblquote We will take a look at how we can (in his words) \ldblquote sculpt the introductory statistics course\rdblquote to make it not only intellectually stimulating but also enjoyable. We will consider the steps that one might follow in redesigning a course that accomplishes both purposes. These include the:\par
\pard\fi-360\li720\tx720\f1\'b7\tab\f0 Approach should we can adopt \par
\f1\'b7\tab\f0 Process to follow \par
\pard\fi-360\li720\f1\'b7\tab\f0 New opportunities \par
\f1\'b7\tab\f0 Choice of topics and their priorities \par
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\pard Closing Keynote ~ \b Restructuring Intro Stats: Changing the Image of Statistics Part II\emdash How Should We \lquote Teach\rquote ?\par
\b0 If we are to change the currently perceived negative image of the first course in statistics we must also address our approach to \lquote teaching\rquote it. The research and anecdotal literature indicates that lecturing has a role in the learning process but other approaches may be much more important. We need to transfer responsibility for learning to students and re-invent ourselves as facilitators. We need to make statistics relevant, interesting, and fun. How do we do this (especially if we are not \lquote hams\rquote !)? In this closing address I will discuss pedagogical techniques that have been found to be effective in promoting successful learning. These include: \par
\pard\fi-360\li720\tx720\f1\'b7\tab\f0 Using technology appropriately and efficiently\par
\pard\fi-360\li720\f1\'b7\tab\f0 Using assessment instruments with rapid feedback\par
\f1\'b7\tab\f0 Promoting a conducive atmosphere for learning\par
\f1\'b7\tab\f0 Sequencing of topics that enhances student understanding \par
\f1\'b7\tab\f0 Hands-on, collaborative group work on activities and projects student surveys generating datasets that are relevant to students\par
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Angel Andreu\b0 , Monroe Community College\par
\cf0 Breakout Session ~ \b SAT Scores and First Semester GPA \b0 (A Case Study)\b\par
\b0 Using the basic statistics taught in an introductory statistics course, we'll try to answer the age-old question of whether SAT scores can predict first semester GPA at a comprehensive community college. \par
(This session is for all) \par
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\pard\cf1\b 3 G Rex Bryce\b0 , BYU\par
\pard\li-26\tx4316\tx7904\b 9 Roger Hoerl\b0 , GE Global Research\par
\b 12 Ronald Snee\b0 , Tunnell Consulting\par
\pard\tx916\tx1832\tx2748\tx3664\tx4580\tx5496\tx6412\tx7328\tx8244\tx9160\tx10076\tx10992\tx11908\tx12824\tx13740\tx14656\cf0 General Session ~ \b A Statistical Thinking Approach to Introductory Statistics: Theory and Practice\par
\b0 This session will discuss the prospect of basing introductory statistics courses on statistical thinking, i.e., fundamental concepts of statistics, as opposed to statistical methods or calculations. We begin with an analysis of typical issues in introductory statistics courses, and identify root causes. For example, we suggest that lack of a "big picture view" of what statistics is, and having no means of integrating the various tools into an overall approach to scientific inquiry, naturally leads to student's viewing statistics as a miscellaneous collection of isolated tools. We have found that statistical thinking, with its emphasis on core concepts, can be used to provide this unifying theme, as well as to address several other key issues. After suggesting the use of statistical thinking as the basis of intro courses, we provide data from two actual courses taught using this approach at BYU. These data, both on what was actually done, and how it was received by students, provides significant evidence in favor of this approach. This session serves as an introduction to the workshop immediately following at 3:30 PM and the breakout session on Friday at 10:15 AM. \par
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Workshop ~ \b The Theory Behind A Statistical Thinking Approach\b0\par
This workshop will delve deeper into the use of statistical thinking as the basis for introductory statistics. We begin by discussing the theory underlying a statistical thinking approach to introductory statistics. This theory is based on existing educational and behavioral research. Once this theory is presented, attendees will have the opportunity to raise questions and concerns, and make suggestions for what such a course should look like, and how it should be implemented. Questions and issues that we are unable to discuss in this session will be taken up in the breakout session scheduled for Friday at 10:15 AM. (Admission to this session is by reservation. 105 Minutes)\par
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Breakout Session ~ \b Open Discussion of Potential Issues With the Statistical Thinking Approach\b0\par
\pard\brdrb\brdrdb\brdrw15\brsp20 \tx916\tx1832\tx2748\tx3664\tx4580\tx5496\tx6412\tx7328\tx8244\tx9160\tx10076\tx10992\tx11908\tx12824\tx13740\tx14656 This breakout is intended to be an open, informal, interactive discussion of potential issues with statistical thinking approach to introductory statistics. The first topics of discussion will be issues that were raised in the workshop on this topic earlier in the conference. Issues may relate to the fundamental approach, to the courses actually offered at BYU, or to unique situations at different institutions represented by the attendees. Following this, attendees will have the opportunity to ask additional questions, and make their own suggestions for comment by others. \par
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Floyd Bullard\b0 , North Carolina School of Science and Mathematics\par
\cf0 Breakout Session ~ \b The AP Statistics Curriculum: What's In, What's Out, and Why\tab\par
\b0 The normal approximation to the binomial is in, but the continuity correction is out. Linear regression on one variable is in, but multiple regression is out. Simulations are in. Many computations are out. Why were these and other choices made and what kind of curriculum does that produce? In this session we'll examine (though we won't resolve it to everyone's satisfaction!) the underlying question: "What should a student know after completing a first-year statistics course?" (This session may be of particular interest to AP statistics teachers, but it is intended for everyone.)\par
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Breakout Session ~ \b AP Statistics Sharing Session \par
\b0 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 three years. (This session is primarily for high school teachers, but all are welcome.)\par
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Breakout Session ~ \b Simulations Are Worth the Classroom Time\par
\b0 A commonly cited reason for not using simulations in the classroom is that they take too long. Why have students do simulations to learn about, say, a chi-square statistic, when you can cover the material in far less time with a lecture or demonstration? In this session, a case will be made for student-designed and -performed simulations as an essential tool for giving students an understanding of the critical idea of a sampling distribution. Three simulation activities will be described, using both manipulatives (dice) and technology (the TI-83 calculator). (Although the technology in this session will be the TI-83, a high school standard, the session is open to anyone and no experience with any particular technology is expected.) \par
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Breakout Session ~ \b Paper Helicopters: An Extremely Versatile Data Collection Tool \par
\b0 In this session we'll look at a paper helicopter first used by George Box to have his students collect data. The helicopter is virtually free and easy for students to make on their own and takes with them out of class (and thus conduct experiments on their own time). It can be used to collect either numerical or categorical data that can be used for about any kind of inference typical in a first-year statistics course, including hypothesis tests or confidence intervals comparing means or proportions, chi-square tests, and linear regression. (This session is for both high school and college instructors.)\par
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Ruth Carver\b0 , Germantown Academy \cf0 [Sponsored by Key College Publishing]\cf1\par
\cf0 Workshop ~ \b Using Fathom Dynamic Statistics Software to explore Sampling Distributions, the Central Limit Theorem, Confidence Intervals and the Robustness of the t-procedure\par
\pard\brdrb\brdrdb\brdrw15\brsp20 \tx916\tx1832\tx2748\tx3664\tx4580\tx5496\tx6412\tx7328\tx8244\tx9160\tx10076\tx10992\tx11908\tx12824\tx13740\tx14656\b0 We will start with a hands-on activity to help students understand sampling distributions and the Central Limit Theorem. We will then extend this activity by using the simulation features of Fathom Dynamic Statistics Software to investigate the effect of sample size on the mean and standard error of a sampling distribution. Our original activity will then be extended to constructing and understanding confidence intervals. The simulation features of Fathom will be used as an aid in understanding what can and cannot be said about a particular confidence interval, the meaning of a particular confidence level and the robustness of the t-procedure when conditions involving sample size have not been met. No familiarity with Fathom Dynamic Statistics Software is assumed. Participants will be given a multi-platform disk containing all simulations for use with their students. (All) (Admission to this session is open. 90 minutes)\par
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Robert Gould\b0 , UCLA\par
\pard\cf0 Breakout Session ~ \b How Statistics differs from Math: beyond mean, median and mode.\par
\b0 Although the intersection between Statistics and Mathematics is quite broad, there is sufficient "extra-mathematical" content in Statistics to make it challenging for a first-time statistics teacher. Mathematics provides a wonderful foundation for learning Statistics, but it is not enough. Math teachers preparing to teach statistics will need to master a new set of skills and concepts. I will present some examples of this "extra-mathematical" content in the context of an epidemiology case study. A sub-theme will be the role of software to teach statistics and, in keeping with this sub-theme, I will demonstrate the use of Fathom to gain insight into the questions raised by the case study. (No prerequisite statements. The ideal audience would be beginning statistics teachers trained in mathematics. Beyond that, it is appropriate for all of the categories HS-non AP through 4 -yr college.)\par
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Breakout Session ~ \b INSPIREd: Report on an NSF-funded project to prepare first-time AP Statistics teachers\par
\pard\b0 The AP Statistics exam has grown exponentially since its founding a few years ago. That growth has fueled a strong demand for AP Statistics teachers and yet there are few teacher-training programs for statistics teachers. The INSPIRE program is funded by the NSF and a joint effort between statisticians and statistics educators at the ASA, Cal Poly San Luis Obispo and UCLA to provide a sustained, in-depth content course targeting beginning AP Stats teachers. As of this summer, one cohort of teachers has finished the first year (a workshop followed by a distance learning course) and are starting their second year (a "practicum" with a statistician mentor). Meanwhile, a new cohort is beginning the program. I'll discuss the design of the program and critically evaluate its achievements. (No pre-requisite statements. The session would be best for HS-non AP, HS AP.)\par
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Mary Harrison\b0 , Virginia Beach City Public Schools\par
\cf0 Breakout Session ~ \b Using the TI-83/84 in the statistics classroom\tab\par
\b0 This session is designed to show how using a TI-83/84 calculator will enable the instructor to focus on the meanings of statistical calculations rather than the arithmetic involved in doing the calculations. By the end of the session the participant should be able to enter data and use the calculator to perform basic descriptive statistics for univariate and bivariate variables, calculate probabilities for binomial, geometric and normal distributions, confidence intervals, and tests of inference involving Student's t, z, and chi-square. (No previous knowledge or experience with Texas Instrument calculators is necessary.)\par
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Breakout Session ~ \b Using the TI-89 in the statistics classroom\tab\par
\b0 This session is designed to show how using a TI-89 calculator will enable the instructor to focus on the meanings of statistical calculations rather than the arithmetic involved in doing the calculations. By the end of the session the participant should be able to enter data and use the calculator to perform basic descriptive statistics for univariate and bivariate variables, calculate probabilities for binomial, geometric and normal distributions, confidence intervals, and tests of inference involving Student's t, z, and chi-square. The 89 has some built-in enhancements over the 83 that will be explored as well. (No previous knowledge or experience with Texas Instrument calculators is necessary.)\par
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Brad Hartlaub\b0 , Kenyon College \par
\pard\tx916\tx1832\tx2748\tx3664\tx4580\tx5496\tx6412\tx7328\tx8244\tx9160\tx10076\tx10992\tx11908\tx12824\tx13740\tx14656\cf0 Breakout Session ~ \b A Snapshot of the AP Statistics Program\b0\par
Brad will compare growth rates in AP Statistics with those in AP Calculus, address general trends on recent exams (including the 2004 exam), and make some recommendations for teachers before a general question and answer session about the AP Statistics Program. Questions regarding curricular issues, course content, the use of statistical software, test development, the exam, rubrics, the annual reading, grade setting, course projects, post-exam activities, or professional development opportunities are most welcome. Session 1 \endash (Pre-requisite: Interest in AP Statistics; HS AP, 2-yr college, 4-yr, college) \par
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Breakout Session ~ \b Exposing Students to Statistical Methods Based on Counts and Ranks\b0\par
Far too often students leave an introductory statistics course thinking that statistical inferences are impossible OR very difficult if the normal model is not applicable to the data. With current technology, students can focus on understanding these competing methods based on counts and ranks without getting caught up in all of the nitty-gritty details. We will focus on one-sample and two-sample location problems which deal with statistical inferences for population medians rather than population means. If time permits, other problems will be considered. (Pre-requisites: Knowledge of basic methods in statistical inference e.g., one-sample t test, paired t test, and the two-sample t-test; HS-non AP, HS AP, 2-yr college, 4-yr college)\par
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Peter Holmes\b0 , Nottingham Trent University \par
\pard\nowidctlpar\tx560\tx1120\tx1680\tx2240\tx2800\tx3360\tx3920\tx4480\tx5040\tx5600\tx6160\tx6720\cf0 After-dinner ~ \b Assessment in Statistics: A two-edged sword \tab\b0\par
It is often asserted that we have to assess students' work in statistics in order to maintain standards. Unfortunately the evidence is that many of the questions we ask and the overemphasis on assessment for qualifications can have the effect of encouraging statistical illiteracy. In this talk I want to look at this problem and consider whether we can learn anything for statistical education from statistical approach to quality improvement and see how we can use assessment to improve learning. (No pre-requisites; relevant to HS non-AP, HS AP, 2 yr college & 4 yr college)\par
\pard\page Breakout Session ~ \b Basic Statistics Courses: boring and irrelevant or true and useful?\tab\par
\b0 Student reaction to many of our basic introductory statistics courses is often not favourable. There have been many suggestions for changing this and many colleges have introduced new ideas. Maybe we need a radical look at the nature of statistics as used in practice and consider the possibility of different basic courses, with different aims, for different groups of students. This talk raises some different possibilities for discussion. (no pre-requisites and is more relevant to 2 year and 4 year college; less relevant to HS)\par
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Carl Lee\b0 , Central Michigan University\par
\pard\cf0 Breakout Session ~ \b The Issues of Student Attitudes and Motivations in Introductory Statistics\par
\b0 The difficulties in teaching and learning introductory statistics have received considerable attention by educators and professional organizations. Review of the research literature suggests that factors associated with the difficulty may include the cognitive domain on the ability of learning, the affective domain on beliefs and attitudes, and the metacognitive domain on motivation and strategies of learning. There has been considerable amount of attention paid to pedagogical issues of teaching introductory statistics. It typically assumes that through innovative teaching pedagogy, students will be more interested and motivated to learn. However, this assumption may not hold as what educators have originally anticipated. In this presentation, we will turn our attention to the less investigated learning domain, namely, the affect and metacognitive domain. The findings from two studies will be shared. One is an interview study conducted in four different institutions to investigate the issues related to motivations and expectations. The other is a survey study conducted in two institutions for four semesters. We will first share the findings from both studies, discuss the implications on teaching, and then propose some strategies that may be useful for developing active learning environments. \par
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\b0 Workshop ~ \b Planning and Assessing Student Learning Outcomes for Statistics\par
\pard\tx916\tx1832\tx2748\tx3664\tx4580\tx5496\tx6412\tx7328\tx8244\tx9160\tx10076\tx10992\tx11908\tx12824\tx13740\tx14656\b0 Learning outcomes usually refer to four domains of educational progress and the end results of learning. These four domains are knowledge, skills, affective domains and the domain of values and ethics. Learning outcomes assessment can be conducted at several levels for different learning goals and objectives, which include the institutional level, the college level, the program level and the classroom level. At each level, it is a cycle typically including the planning stage, the implementation stage and the action-taken stage.\~ It is similar to the Deming\rquote s PLAN-DO-STUDY-ACT quality improvement Cycle, which contently asks and addresses the two questions: (1) Are we doing the right things, and (b) Are we doing the things right? In this workshop, we will first discuss an assessment framework and address some potential gaps in a typical student learning outcome process based on the framework. The remaining time will focus on the development of an assessment plan at the program level for statistics major and the applications of tools for the classroom assessment for statistics courses. A hands-on approach will be used to engage each participant to develop his/her own assessment plan and determine the appropriate direct and/or indirect measures for assessing the objectives and to practice some commonly used classroom assessment tools. (Admission to this session is by reservation. 105 Minutes)\par
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Kirk Steinhorst\b0 , University of Idaho\par
\pard\s5\cf0 Breakout Session ~ \b Intro stat should not be like drinking water through a fire hose\par
\pard\b0 Introductory statistics books have grown larger and larger over the years.\~ There are too many topics and too much detail.\~ The instructor must be ruthless in deciding exactly what must be covered.\~ Many courses struggle because there is too much material.\~ But - what to cut?\~ I present the essential topics that I have refined over the last 30 years from the myriad topics available and justify my choices.\~ The resulting syllabus is slim and trim.\~ Students need to see the big picture and the beauty of introductory statistics.\~ They should not get bogged down in details.\~ I illustrate specific choices that the instructor must make in descriptive statistics, probability, and inference.\~ Statistics does not need to be the most dreaded course.\~ Simplicity is beauty in this case.(All)\line\~\par
\pard\s5 Breakout Session ~ \b Testing and assessing in a modern statistics methods course\par
\pard\brdrb\brdrdb\brdrw15\brsp20 \s5\b0 Modern technology makes statistical computations trivial.\~ However, students need to do some calculations by hand so that they understand what the calculator or computer is doing.\~ How can we assess students' knowledge using appropriate technology?\~ The answer is a combination of the right kind of homework, in-class activities, and takehome/in-class tests.\~ Homework should be conceptual mixed with problems based on real data requiring real calculation.\~ In-class activities should explore concepts and include simple calculations.\~ The takehome/in-class test consists of problems on which students can collaborate and use the calculator or computer followed by an in-class portion where students use the takehome to answer a series of related or derivative questions--thus being individually responsible in the end.\~ Examples are given of each activity. (All)\line\par
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Linda Young\b0 , University of Florida\~\par
\cf0 Panel Discussion ~ Abstract later, after panel is formed \endash probably not until April\par
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Since the design of a study has a major effect on what conclusions can and cannot be drawn from that study, basic design principles should be included in any introductory course, but which ones and at what depth? During this interaction session, basic design issues, such as the choice of experimental unit, random selection of units from a population, random assignment of treatments to the experimental units, sample size, and power, and their importance (or lack thereof) in an introductory statistics course will be discussed.\par
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Speaker?\b0 , Minitab, Inc.\par
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