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0@RG:G2004 Speakers, Titles & Abstracts
This page contains the biographies, titles and abstracts forALL speakers. Click on the speaker's name to find each speaker's biography, titles and abstracts.
These same titles and abstracts are also listed by scheduled session on the webpage titled, "2004 Titles & Abstracts by Session".
The 2004 Speakers
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "harkness" William Harkness, Professor of Statistics, Penn State University
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "andreu" Angel Andreu, Research, Monroe Community College
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "bryce" G Rex Bryce, Professor of Statistics, Associate Dean, BYU
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "bullard" Floyd Bullard, Mathematics Department, North Carolina School of Science and Mathematics
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "carver" Ruth Carver, Mathematics Teacher, Germantown Academy
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "gould" Robert Gould, Director, Center for Teaching Statistics, UCLA
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "harrison" Mary Harrison, Mathematics Teacher, Virginia Beach City Public Schools
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "hartlaub" Brad Hartlaub, Professor of Mathematics, Kenyon College
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "hoerl" Roger Hoerl, GE Global Research
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "holmes" Peter Holmes, Senior Consultant, RSS Centre for Statistical Education, Nottingham Trent University
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "krasowitz" Jason Krasowitz, Academic Sales Representative, Minitab Inc.
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "lee" Carl Lee, Professor of Statistics, Central Michigan University
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "snee" Ronald Snee, Tunnell Consulting
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "steinhorst" Kirk Steinhorst, Professor of Statistics, University of Idaho
HYPERLINK "http://web.monroecc.edu/beyond/STandA" \l "young" Linda Young, Professor of Statistics, University of Florida
William Harkness, Penn State University ~ Keynote Speaker
Bill Harkness: Received B.S. (1955) and M.A. (1956) degrees in Mathematics and Ph.D. in Statistics (1959) from Michigan State University. Appointed as an Assistant Professor at Penn State in 1959. Served as Head of the Department of Statistics from 1969 through 1987.Since 1987 his primary interests have been in statistical education. He has taught the basic introductory statistics (Stat 200) continuously after he escaped the headship, except for one semester (Fall 1999) when he was on sabbatical but working on restructuring Stat 200 with the support of a grant from the Pew Foundations Center for Academic Transformation. He is currently working on a similar project for engineering and biological statistics with the support of a grant from NSF. He officially retired in June 2002 but continues to work full time (without salary).
Opening Keynote ~ Restructuring Intro Stats: Changing the Image of Statistics Part I--What Should We Teach? ~ 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 There is an interesting, subtle, beautiful introductory statistics course (out) there, but it is often obscured by topics, notation, terminology, and tedium that dont belong. We will take a look at how we can (in his words) sculpt the introductory statistics course 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:
Approach we can adopt
Process to follow
New opportunities
Choice of topics and their priorities
Closing Keynote ~ Restructuring Intro Stats: Changing the Image of Statistics Part IIHow Should We Teach? ~ If we are to change the currently perceived negative image of the first course in statistics we must also address our approach to teaching 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 hams!)? In this closing address I will discuss pedagogical techniques that have been found to be effective in promoting successful learning. These include:
Using technology appropriately and efficiently
Using assessment instruments with rapid feedback
Promoting a conducive atmosphere for learning
Sequencing of topics that enhances student understanding
Hands-on, collaborative group work on activities and projects student surveys generating datasets that are relevant to students
Angel Andreu, Monroe Community College
Angel Andreu is Assistant Director of Institutional Research. He started working at MCC in 1988 in the Department of Mathematics as an instructor. In 1999 he was promoted to Associate Professor, and in the Fall 2002 he moved into the Office of Institutional Research. While in the mathematics department he regularly taught elementary statistics I and II and had made his statistics courses Writing Intensive (a program at MCC which Angel was coordinator for two years: HYPERLINK "http://www.monroecc.edu/depts/wac/index.htm" HYPERLINK "http://www.monroecc.edu/depts/wac/index.htm" http://www.monroecc.edu/depts/wac/index.htm). In 2000, Patricia Kuby and Angel developed a course titled, Introduction to Data Analysis with Excel, which became MTH 166 in the Fall 2001. Angel has also attended the first six Beyond the Formula conferences and believes that these are the best two days you will spend in summer (unsolicited). Academic credentials: BS in Mathematics, Florida International University; MS in Mathematics, University of Arkansas at Fayetteville.
Breakout Session ~ A Case Study: SAT Scores and First Semester GPA ~ 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. (This session is for all)
G Rex Bryce, BYU
Dr. Bryce holds a Ph. D. in Statistics, is a Fellow of the American Statistical Association (ASA), and a senior member of the American Society for Quality. He has been amember of the faculty at Brigham Young University since 1972 where he is Professor of Statistics. He has served as Chair of the Department of Statistics, and is currently Associate Dean in the College of Physical and Mathematical Sciences. Bryce's research interests are in the application of statistical science to quality and productivity measurement and improvement, and in the improvement of undergraduate statistics education. He was a leader in the development of the ASAs curriculum guidelines for undergraduate degrees in statistics. He has worked with industry either directly or as a consultant since 1963. Bryce has worked with industries as diverse as sporting goods, semiconductors, and aircraft engines to help them improve the quality of their products and the productivity of their people. His consulting practice has involved the creation of management environments and organizations for continuous improvement, the application of statistical methods to process and product improvement, and the design, analysis and interpretation of experiments and observational studies.
Roger Hoerl, GE Global Research
Roger Hoerl has been with GE for 8 years, where he leads the Applied Statistics Lab at GE Global Research. This group consists of 14 statisticians who develop analytical technology in support of product and service development at each GE business. He received his Ph.D. in Statistics from the University of Delaware, and has been on the adjunct faculty of both the University of Delaware and Drexel University. He has been named a Fellow of the American Statistical Society and American Society for Quality, was elected to the International Statistical Institute, and has received both the Brumbaugh Award and Hunter Award from the American Society for Quality. His introductory text Statistical Thinking: Improving Business Performance, co-authored with Ron Snee of Tunnell Consulting, was described as "probably the most practical basic statistics textbook that has ever been written within a business context." by the journal Technometrics.
Ronald Snee, Tunnell Consulting
Ron Snee is a Principal with Tunnell Consulting where he provides guidance to senior executives in their pursuit of improved business performance using Six Sigma process improvement initiatives and other needed approaches that produce bottom line results. Prior to this assignment Ron used his broad experience and expertise working as a Senior Management Consultant and Six Sigma Deployment Leader with Sigma Breakthrough Technologies, Inc. His work there focused on the effective deployment of Six Sigma initiatives including strategic planning, project selection, Executive, Champion and Master Black Belt Workshops and deployment guidance and assessment. Ron recently coauthored Statistical Thinking Improving Business Performance with Roger W. Hoerl of General Electric.
Ron received his BA in Mathematics from Washington and Jefferson College and MS and PhD degrees from Rutgers University in Applied and Mathematical Statistics. He is a Fellow of the American Society of Quality, the American Statistical Association, and the American Association for the Advancement of Science. He has been awarded ASQs Shewhart Medal, the Societys highest award, as well as numerous other awards and honors. He is a frequent speaker and has published 3 books and more than 150 papers in the fields of performance improvement, quality, management, and statistics.
General Session ~ A Statistical Thinking Approach to Introductory Statistics: Theory and Practice ~ 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 students received it, 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.
Workshop ~ The Theory Behind A Statistical Thinking Approach ~ 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)
Breakout Session ~ Open Discussion of Potential Issues With the Statistical Thinking Approach ~ 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.
Floyd Bullard, North Carolina School of Science and Mathematics
Floyd Bullard earned a B.S. in applied mathematics from the Johns Hopkins University in 1991 and a M.S. in statistics from the University of North Carolina at Chapel Hill in 1999. He has taught high school math, including statistics, for eight years and is presently on leave from the North Carolina School of Science and Mathematics to study in a statistics doctoral program at Duke University. After completing the program, Floyd intends to return to teaching. Floyd has been a statistics AP exam reader for two years; he has written several articles for the AP Central web site; he teaches an on-line course through UCLA for new statistics teachers; and he is involved in the Statistics Leadership Institute, a group of high school statistics teachers who have studied and worked together for four years to provide statistics teachers around the country with resources and workshops. In 2002 Floyd helped to teach a three-week summer workshop covering the entirety of the AP statistics curriculum. Floyd is very happy to be participating in the Beyond the Formula conference for a second year.
Breakout Session ~ The AP Statistics Curriculum: What's In, What's Out, and Why ~ 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.)
Breakout Session ~ Paper Helicopters: An Extremely Versatile Data Collection Tool ~ 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 take with them out of class (and thus they may 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.)
Ruth Carver, Germantown Academy [Sponsored by Key College Publishing]
Ruth Carver has been a reader for the AP Statistics exam since 2001. She coauthored Preparing for the AP Statistics Exam,Pearson Addison Wesley Education, Inc., 2004 and also co-authored the College Boards official AP Statistics Web site.She has been the lead presenter for Key Curriculum's Fathom Summer Institute for the past three years.
Workshop ~ Using Fathom Dynamic Statistics Software to explore Sampling Distributions, the Central Limit Theorem, Confidence Intervals and the Robustness of the t-procedure ~ 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)
Robert Gould, UCLA
Robert Gould received his Ph.D. in Mathematics from the University of California, San Diego in 1994. His undergraduate degree is in applied mathematics from Harvey Mudd College, 1987. In 1994 he worked as an adjunct assistant professor in the UCLA Department of Mathematics, and in 1998 joined the newly created UCLA Department of Statistics as the director of the Center for Teaching Statistics. He is interested in inserting data analysis into as much of the statistics curriculum as possible, and as part of this effort was funded by the NSF to establish an undergraduate statistics computing lab and is collaborating with his colleagues to write a lab manual of data analysis exercises for introductory statistics students. He has been an AP reader for the last four summers, and in 1999 began offering a data analysis course for experienced high school statistics teachers. Along with Roxy Peck, he is co-principal investigator of the INSPIRE project, which is a year-long distance course designed to teach statistics to first-time AP Statistics teachers.
Breakout Session ~ How Statistics differs from Math: beyond mean, median and mode ~ 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.)
Breakout Session ~ INSPIREd: Report on an NSF-funded project to prepare first-time AP Statistics teachers ~ 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.)
Mary Harrison, Virginia Beach City Public Schools
Mary Harrison earned an A.B. in mathematics from Goucher College at Baltimore, Md. in 1974 and an M.S. in mathematics from the University of Central Florida at Orlando in 1982. She first taught high school statistics at Ryan Upper School in Norfolk in 1982 and has taught AP Statistics at Salem High School in Virginia Beach, Va. since 1997. She also taught introductory statistics at Charleston Southern University at South Carolina. She developed and presented a statistics workshop for middle school math teachers, is a member of a high school forum that meets monthly during the school year and once during the summer, and has presented a session on using Minitab as an integral part of statistics instruction. Mary is an avid scouter, having been a Girl Scout leader for the last twelve years and an advisor for a co-ed high adventure Venture Crew for the last four.
Workshop ~ Using the TI-83/84 in the statistics classroom ~ 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.)
Workshop ~ Using the TI-89 in the statistics classroom ~ 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.)
Brad Hartlaub, Kenyon College
Brad Hartlaub joined the Kenyon faculty in 1990. He is a nonparametric statistician, and his research deals with rank-based tests for detecting interaction. He has published research articles on count or rank based statistical methods in the Journal of Nonparametric Statistics, The Canadian Journal of Statistics, and Environmental and Ecological Statistics. He is currently serving as the Chief Reader of the AP Statistics Program and has been an active member of the American Statistical Association's Section on Statistical Education. He has also been leading professional development workshops and institutes for AP Statistics Teachers since 1995. He has served the College as Chair of the Mathematics Department, Chair of the Division of Natural Sciences, a member of the Self Study Committee, and a member of the Committee on Academic Standards. He has received research grants to support his work with undergraduate students from the Andrew W. Mellon Foundation and the Council on Undergraduate Research. His students have presented their research at national meetings and 2003 Posters on the Hill, sponsored by the Council on Undergraduate Research. He has also received an author grant from Addison Wesley Longman to write an introductory textbook, Intuitive Introductory Statistics, with Douglas A. Wolfe at Ohio State University.
Breakout Session ~ A Snapshot of the AP Statistics Program ~ 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 (Pre-requisite: Interest in AP Statistics; HS AP, 2-yr college, 4-yr, college)
Breakout Session ~ Exposing Students to Statistical Methods Based on Counts and Ranks ~ 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)
Peter Holmes, Nottingham Trent University
Peter Holmes graduated from the University of Manchester in 1959 with a B.Sc. and postgraduate certificate in education. For eight years he taught mathematics and statistics at a secondary school before becoming a senior lecturer at a college of education for training teachers. During this time he obtained an M.SC. in Probability and Statistics from the University of Sheffield. He introduced and was the first chairman of examiners for a GCE A-level in Statistics (the English equivalent to AP Statistics)
In 1975 he became Director of the Schools Council Project on Statistical Education (a major curriculum development project for teaching statistics to students aged 11 to 16) and then became the founding Director of the Centre for Statistical Education at the University of Sheffield. He has directed many projects on the teaching of statistics, both at school and university level. He was the first editor of the journal Teaching Statistics, and is currently a member of the editorial board and a trustee of the Teaching Statistics Trust.
He has spoken at many international conferences on the teaching of mathematics and statistics, and was a plenary speaker at the First Scientific Meeting of the International Association for Statistical Education in Perugia in 1996 and at the 6th International Conference on Teaching Statistics in Cape Town. 2002. He has been involved with several teacher-training projects in both the United Kingdom and the United States.
He is currently Senior Consultant at Nottingham Trent University both to the RSS Centre for Statistical Education and to the Learning and Teaching Support Network for Mathematics, Statistics and OR (which works at University level) with particular responsibility for work on assessment in Statistics following his research work on matching education and assessment with employment needs in Statistics (the MeaNs Project)
After-dinner ~ Assessment in Statistics: A two-edged sword ~ 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 a 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)
Breakout Session ~ Basic Statistics Courses: boring and irrelevant or true and useful? ~ 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)
Jason Krasowitz, Academic Sales Representative, Minitab Inc.
Breakout Session ~ Whats New in Minitab 14 Graphics ~The graphics in release 14 have been significantly improved, and this class will take you through those changes.Starting with the new graph galleries, users can specify the type of graph they want to create which simplifies subsequent dialog boxes. We will demonstrate how graph editing is now easily done after graphs are created via double-clicking on an element or selecting it using the new graph editing toolbar. Now, elements such as means, interval bars, or fitted lines can be added to an existing graph, eliminating the need to create duplicate graphs. The improved graph layout tool allows you to quickly plot more than one graph on a page, and specifying subsets of data to use in a graph is now possible using data options in the dialog boxes. Since graphs in Release 14 are now tied to the data, well show you how to update your graphs when data changes, either automatically or manually. There are also some new graphs to version 14, including the Individual Value Plot, and the Empirical Cumulative Distribution plot. Visualize 3-D data using our new rotating plots. Other enhancements such as creating similar graphs, duplicating graphs and saving the command language will also be discussed. (All)
Carl Lee, Central Michigan University
Carl Lee graduated from the Department of Statistics Iowa State University in 1984. He is a full professor of Statistics in the Department of Mathematics and a senior research fellow at the Center for Applied Research & Technology at Central Michigan University. He is an elected member of the International Statistical Institute. He is the president of the Mid-Michigan Chapter of American Statistical Association. He currently serves as an advisory board member of the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE) and an associate editor of the CAUSE online publications. His research interest in statistics education includes studying how affective and metacognitive factors affect student learning and how to develop active learning environments. Lee serves as a consultant for the Regional Education School District to evaluate school climates and to develop drug-free and violence prevention programs for Middle and High schools in the Mid-Michigan area. Lee organized the summer workshop on teaching Mathematics using Technology for secondary school teachers at Central Michigan University. Lee was the University Assessment Coordinator. He regularly conducted assessment workshops for different departments and gave talks in different institutions on developing active learning environments in statistics.
Breakout Session ~ The Issues of Student Attitudes and Motivations in Introductory Statistics ~ 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.
Workshop ~ Planning and Assessing Student Learning Outcomes for Statistics ~
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 Demings PLAN-DO-STUDY-ACT quality improvement Cycle, which constantly 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)
Kirk Steinhorst, University of Idaho
Kirk Steinhorst has taught statistics since 1971 at all levels from introductory statistics to the theory of linear models. He has taught at San Diego State, Colorado State, Texas A&M, and the University of Idaho. His appointments include biometrician positions in grassland ecology and the agricultural experiment station as well as conventional faculty positions in mathematics and statistics. He has been doing research in teaching statistics since 1990. He has extensive consulting experience in fisheries, forestry, wildlife, crop management, anthropology, hydrology, and marketing.
Breakout Session ~ Intro stat should not be like drinking water through a fire hose ~ 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)
Breakout Session ~ Testing and assessing in a modern statistics methods course ~ 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)
Linda Young, University of Florida
Linda Young received her B.S. and M.S. in mathematics from West Texas A&M University and her Ph.D. in statistics from Oklahoma State University. Having served on the faculty at both Oklahoma State University and the University of Nebraska, she became Associate Chair of the Department of Statistics in the College of Medicine at the University of Florida in July 2003. Linda has collaborated with researchers in agriculture, ecology, the environment, and medicine, publishing more than 60 refereed papers, several book chapters, and a book. She has served in numerous capacities in the statistical community, including President of the Eastern North American Region of the International Biometric Society, Vice-President of the American Statistical Association, and Chair of the Committee of Presidents of Statistical Societies. Linda was a member and chair of the ASA/NCTM Joint Committee on the Curriculum in Statistics and Probability and a team leader for several Elementary Quantitative Literacy Workshops in Nebraska. She taught the experimental design component of the North Carolina School of Science and Maths summer statistics institute for AP teachers, returning the two following summers to review curriculum developed by these teachers. Linda is coordinator of the judging for the American Statistics Project Competition. She has served as both a reader and table leader for AP Statistics and is currently chair of the AP Statistics Test Development Committee.
Panel Discussion ~ Abstract later, after panel is formed probably not until April
Breakout Session ~ Design of Studies: Important Concepts ~ 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 interactive 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.
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