Megan R. Hall and Ginger Holmes Rowell
Middle Tennessee State University
Journal of Statistics Education Volume 16, Number 2 (2008), www.amstat.org/publications/jse/v16n2/rowell2.html
Copyright © 2008 by Megan R. Hall and Ginger Holmes Rowell all rights reserved. This text may be freely shared among individuals, but it may not be republished in any medium without express written consent from the authors and advance notification of the editor.
Key Words: American Statistical Association; Curriculum Guidelines; Teaching Materials; Grant Projects.
This paper describes 25 National Science Foundation supported projects that have innovations designed to improve education for students majoring or minoring in statistics. The characteristics of these projects and the common themes which emerge are compared with the American Statistical Association’s (ASA) guidelines for developing statistics education curricula for majors and minors and for teaching the corresponding statistics courses. Through this analysis, we are able to see how the last decade of NSF supported projects in statistics education exemplify these ASA guidelines.
During the last decade, the National Science Foundation (NSF) has provided financial support for a number of projects which emphasize improving undergraduate statistics education. Concurrently, educators and researchers in the field have been defining and revising curriculum standards that have resulted in nationally recognized guidelines for teaching statistics. Inspired by George Cobb’s 1993 review of twelve NSF sponsored statistics education projects, this paper seeks to review NSF projects from 1993 to 2004 focused on undergraduate statistics education beyond the algebrabased introductory course and to compare the characteristics of those projects with current statistics education recommendations. First, we review the development of the American Statistical Association’s (ASA) (2002) Curriculum Guidelines for Undergraduate Programs in Statistical Science, and then describe some NSF Division of Undergraduate Education (DUE) programs which operated from 1993 to 2004. Throughout that time, NSF has supported over 150 grants which directly affect statistics education, 52 of which focus beyond the algebrabased introductory statistics course. From those 52 grants, 25 projects are discussed in comparison with the ASA Curriculum Guidelines. Through descriptions of these projects, we are able to see how NSF has been supporting projects in statistics education which meet the ASA guidelines.
For many years, the efforts of statistical educators were focused on graduate programs because of the difficulty of the material and the idea that statisticians are defined by the advanced degrees they hold (Minton 1983). However, as technology reduced the difficulty found in statistical analyses, the expected increase in undergraduate statistics programs did not happen (Higgins 1999). The resulting problem was a lack of visibility and acceptance of statistics as a discipline (Minton 1983). The solution was to create viable undergraduate degree programs in statistics (Marquardt 1987).
The American Statistical Association (ASA) has been aware of the need for undergraduate statistics education reform, and since 1979 has made several attempts to set up curriculum guidelines for undergraduate programs in statistics (Snee 1993; Bryce, Gould, Notz, & Peck 2001; Bryce 2002). The most successful of these attempts was the launching of the Undergraduate Statistics Education Initiative (USEI) in 1999, spurred by the February 1999 issue of The American Statistician, in which Robert Hogg and James Higgins addressed the plight of undergraduate statistics education. The USEI group organized a symposium for the 1999 Joint Statistical Meetings for which six papers addressing statistics in undergraduate education were written (Bryce 2002). G. Rex Bryce, Robert Gould, William Notz, and Roxy Peck (2001) addressed the needs of Bachelors of Science students, by recommending skills undergraduates should gain from statistical science programs. These recommendations have been integrated into the ASA’s Curriculum Guidelines for Undergraduate Programs in Statistical Science, which received approval from the ASA Board of Directors on December 2, 2000 (Bryce 2002).
According to ASA’s curriculum guidelines, "Undergraduate statistics programs should emphasize concepts and tools for working with data and provide experience in designing data collection and in analyzing real data that go beyond the content of a first course in statistical methods" (ASA 2002, p. 1). To do this, the ASA guidelines list five skill sets undergraduates should possess upon commencement, modeled after the recommendations set forth by Bryce, et al (2001). They are statistical, mathematical, nonmathematical, computational, and substantive area skills. Statistical skills include statistical reasoning, experimental design, exploratory data analysis, and formal inference procedures, while probability and statistical theory are listed under mathematical skills. Calculus, linear algebra, and rigorous proof methods are also necessary mathematical foundations. Nonmathematical skills, such as communication and collaboration, require students to speak clearly, write well, work well in teams, and organize and manage projects and data collection processes. Such skills require more than a sequence of math courses. Computational skills include familiarity with statistical software, statistical computing, communication software, data management, and algorithmic problem solving. Last but not least, substantive area skills come from experience in an application area such as survey sampling, industrial design, or nonparametric methods. Additionally, the use of real data in meaningful contexts is emphasized along with the synthesis of theory, methods, and applications. Participation in an internship, capstone course, and/or consulting experience can round out students’ statistical experience (Bryce, et al. 2001, ASA 2002).
The National Science Foundation (NSF), with a mission which includes providing "research, guidance, and support for U.S. science and mathematics education," (NSF 1996a, p.6), has also been addressing needs in undergraduate education through its Directorate for Education and Human Resources (EHR). While no part of the science, technology, engineering, and mathematics (STEM) preschool through postgraduate education spectrum is independent, NSF (1996b) believes undergraduate education maintains the entire system because teachers, technical workers, and professional practitioners are educated and prepared at the undergraduate level. Because of the importance of undergraduate education, EHR established the Division of Undergraduate Education (DUE). DUE has supported a number of programs focused on various aspects of undergraduate education including instructional equipment, curricular improvement, technology education, preservice and inservice teacher education, and faculty enhancement. All of the projects and programs reviewed in this article are, or were, supervised by DUE. However, because we cannot cover all DUE programs ever to exist, we have limited this article to programs most directly affecting undergraduate statistics education.
One influential program sponsored by DUE was the Instrumentation and Laboratory Improvement Program (ILI), created "to increase the range and quality of modern laboratory equipment and to provide equipmentbased learning opportunities for undergraduate students," (NSF 1998a, p.8). At its inception in 1985, ILI only accepted applications from fouryear colleges and universities and was later restructured to allow proposals from twoyear institutions. During the program’s first decade, over 4,700 grants, ranging from $5,000 to $100,000, were awarded for the purchase of instructional laboratory equipment (NSF 1998a). ILI operated until 1998, when it was integrated into the current Course, Curriculum, and Laboratory Improvement Program (CCLI) (NSF 1998b). The ILI projects chosen for this article established laboratories and developed materials for their use in statistics courses.
The goal of the Course and Curriculum Development Program (CCD), developed in 1988, was "to revitalize the content, conduct, and quality of undergraduate education in [STEM] through new and innovative approaches to all aspects of the undergraduate learning experience," (NSF 1995, p.11). CCD has contributed to the production of textbooks, software, and other materials, as well as the development of numerous courses and sequences of courses. From its inception until 1996, CCD funded almost 800 grants totaling $102 million. Projects that did not develop curricular materials either promoted or facilitated adoption and adaptation of such materials at other institutions (NSF 1995). The CCD projects selected for this article developed materials for postcalculus introductory statistics, regression analysis, and a capstone course in statistics.CCD was integrated into CCLI in 1998 (NSF 1998b).
The Course, Curriculum, and Laboratory Improvement Program (CCLI), established in 1998, is just as its name implies: a combination of CCD and ILI. At its birth, proposals requesting funds for curricular development and for purchasing instructional laboratory equipment were still accepted (NSF 1998b). But more than just combining two programs, NSF’s goal in creating CCLI was "to stimulate and motivate faculty members so that creative teaching and pedagogical scholarship become a part of the ‘faculty culture’ at all institutions," (NSF 2003a, p.6). To accomplish such a challenging goal, CCLI had four tracks: Educational Materials Development (EMD), Adaptation and Implementation (AI), National Dissemination (ND), and Assessment of Student Achievement (ASA) (NSF 1998b, NSF 2003b).
A goal of the EMD track was to improve undergraduate STEM education and student learning through the development of innovative educational materials like courses, curricula, or laboratory materials that would incorporate effective teaching practices and be suitable for national distribution (NSF 2003a). EMD projects discussed in this paper focus on statistical concepts, real data, computing skills, and consulting experiences and develop technology rich resources for teachers. CCLI’s Adaptation and Implementation (AI) track existed to encourage faculty to adapt exemplary materials, practices, and techniques developed at other institutions and implement them at their own (NSF 2003c). The AI projects described here adapt course materials for first courses for statistics majors, client disciplines, and the entire statistics curriculum. National Dissemination projects were intended to support dissemination of exemplary STEM educational materials through professional development opportunities for faculty, which provide materials, followup activities, networks of faculty, and evaluation protocols (NSF 2003a). These projects have often been workshops or short courses, some of which are described later. Lastly, Assessment of Student Achievement projects promoted the evaluation and assessment of student learning in the STEM disciplines and encouraged the development and use of authentic assessment tools (NSF 2003b). These four tracks of CCLI were retired in fiscal year 2006, making room for a cyclical model of knowledge production and improvement with five supporting components: teaching and learning research, learning materials development, faculty enhancement, innovative materials implementation, and assessment of learning innovations (NSF 2005a).
The Advanced Technological Education (ATE) program targets technician education and encourages projects to include collaboration between two and fouryear colleges, universities, secondary schools, business, industry, or government. Funded projects have addressed curricular development, created Centers of Excellence providing systemsbased approaches to technological education, and performed studies that promoted better understanding of the issues of technological education (NSF 1998b).
From 1988 until 1998, the Undergraduate Faculty Enhancement (UFE) program provided funding for a wide range of development opportunities for college faculty, such as workshops, short courses, and seminars. Over 500 workshop grants, ranging from $10,000 to $500,000, provided faculty opportunities to interact with experts in their fields, learn new experimental techniques, and to explore new teaching methods and technologies (Marder, McCullough, Perakis 2001). In this paper, the UFE projects described connect faculty with industrial statisticians and provide training for faculty teaching preservice teachers and the social sciences. Another program focused on preservice teachers was the Collaboratives for Excellence in Teacher Preparation (CETP), which ran from 1993 until 2001, and promoted the recruitment and development of future teachers (NSF 1999).
A relatively new DUE program is the National Science Digital Library (NSDL), whose goal is to develop a comprehensive repository of the highest quality science, technology, engineering, and mathematics education materials. This powerful collection of learning materials, ranging from kindergarten to the graduate level, provides opportunities for both formal and informal lifelong learning (NSF 2005b) and can be accessed at www.nsdl.org. The Consortium for the Advancement of Undergraduate Statistics Education (CAUSE) statistics education digital library is an NSDL grant project described in this paper.
To provide an update on George Cobb’s review of NSF projects in statistics education sponsored between 1990 and 1992, we searched NSF’s Award Search webpage (www.nsf.gov/awardsearch/) using the keyword "statistics" for projects funded between 1993 and 2004. We found 158 grants whose primary focus was statistics education, while projects indirectly affecting statistics education were left for other articles. Fiftytwo of the 158 grants target students majoring in statistics or studying statistics within a client discipline. Thirtyfive percent of those 52 were funded by Instrumentation and Laboratory Improvement, 23% by CCLI Educational Materials Development, 15% by CCLI Adaptation and Implementation, 13% by Course and Curriculum Development, 6% by Undergraduate Faculty Enhancement, and 2% by each of Advanced Technological Education, CCLI National Dissemination, National Science Digital Library, and Collaboratives for Excellence in Teacher Preparation. The median award value is $63,997. The Appendix lists each grant with additional information such as monetary value, program support, and principal investigator. Additional information about these projects can be found by using the NSF Awards Search webpage (http:www.nsf.gov/awardsearch/) and entering the award number in the "Search Award for" dialog box. We do not claim that this list is exhaustive as we could have unintentionally overlooked grants. If you received funding for a project you believe fits the nature of this article and was not included, please accept our apologies.
The projects discussed in the text of this article were chosen based on several criteria: 1) characteristics that exemplify the recommendations of the ASA Curriculum Guidelines, 2) models of successful NSF projects in the programs they represent, and 3) sufficient available followup information on the project, including the project update found from using the NSF DUE Project Information Resource System (PIRS) webpage search (https://www.ehr.nsf.gov/pirs_prs_web/search/). Specifically, we intend to highlight 25 projects (funded through 31 grants) that focus on conceptual understanding with theoretical background, experience with real problems and real data, computational skills, or communication skills. We have no intention of ranking the quality of these projects, as many times the availability of information determined which projects were included in the discussion. The projects are discussed according to their placement in the curriculum beginning with first courses for majors, followed by advanced courses, courses in client disciplines, learning resources, and faculty enhancement.
A common introduction to statistics for majors and minors is the twocourse mathematical statistics sequence. While these courses provide a strong base in statistical theory, the USEI guidelines insist that a first course also provides students with experience in statistical thinking and literacy as do nonmathematical introductions (Bryce, et al. 2001). Reformers have overlooked this course and its students until recently (Rossman, Chance 2004). NSF funding suggests the same as only onethird of the grants included in this study that address a postcalculus introduction to statistics occurred prior to 2000. Since that time, NSF has sponsored several of these projects, as educators are recognizing the importance of firm foundations for statistics majors and minors.
Two of the funded projects intended for postcalculus introductory statistics are Kyle Siegrist’s "The Probability/Statistics Object Library" (DUE #0089377) and its companion "Virtual Laboratories in Statistics" (DUE #9652870) developed at the University of Alabama, Huntsville. The "Probability/Statistics Object Library" is a collection of free applets and building blocks of applets which are stand alone versions of real experiments intended to illustrate concepts and methods. They can be dropped into web pages, supplemented by text, or modified with additional programming to meet an individual audience’s needs (Siegrist 2005). The "Virtual Laboratories in Probability and Statistics" website is an example of how the Object Library components can be used. Each module in the Virtual Lab contains text explaining probabilistic or statistical content with mathematical background, simulations through interactive applets, and real data sets, involving data analysis and computation. The project focus is to provide students a more wellrounded statistical experience through mathematics, simulation, and data analysis (Siegrist 2000). The projects can be found at http://www.math.uah.edu/stat and require the MathML or MathPlayer programs, which are freely downloadable from the website.
California Polytechnic State University’s Allan Rossman and Beth Chance, with some assistance from Karla Ballman, also received funding for a postcalculus introductory statistics project. Their book, "Introduction to Statistical Concepts, Applications, and Methods" (ISCAM), supported by the NSF grants "A DataOriented, Active Learning, PostCalculus Introduction to Statistical Concepts, Methods, and Theory" (DUE #9950476, #0321973), encourages active exploration of statistical concepts through the use of real data from real studies. Discoverybased, collaborative learning activities introduce concepts and applications through investigation of statistical procedures and properties, while building on students’ mathematical background to teach theory. Probability concepts are only introduced in the context of the statistical ideas they support, technology is used frequently for simulation and computing purposes, and oral and written communication skills are emphasized through projects and reports. Ultimately, the goal of this project is for students to perform the statistical process enough times to be better able to apply their knowledge to a wide array of areas and problems (Rossman, Chance 2004).
These three projects have had a national impact throughout the statistics education community. An example is the NSF grant "Collaborative Research: Adaptation and Implementation of Activity and WebBased Materials into PostCalculus Introductory Probability and Statistics Courses" (DUE #0126401, #0126600, #0126716, #0350724) by Tracy GoodsonEspy, Ginger Rowell, and Leigh Lunsford. This project adapted the materials from the Virtual Labs and from ISCAM to fit the postcalculus level introductory statistics courses at the University of Alabama at Huntsville, Middle Tennessee State University, and Athens State University, respectively (Lunsford 2004).
Deborah Nolan and Terrence Speed, of the University of California, Berkeley, developed a project to teach mathematical statistics using case studies, called "Broadening the Scope of Statistical Education through Technology" (DUE #0127557). They created a computing environment with graphical user interfaces (GUI) and the R programming language. Students applied statistical methods to real problems with real data, experienced computing and simulation exercises, and wrote reports of their analyses (Nolan, Lang 2003). A similar project by Jenny Baglivo, "A Course in Computer Intensive Statistical Methods for Mathematical Sciences" (DUE #9555178), produced a textbook of Mathematica laboratories for emphasizing statistical computing in the mathematical statistics sequence at Boston College (Baglivo 2000).
A variety of advanced courses in statistics are available to undergraduate majors and minors. Some courses focus on specific topics like regression analysis or nonparametric methods, while others cover a broad range of topics in a specific application area, such as business statistics or biostatistics. Capstone courses, internships, or consulting experiences provide students with the necessary practice to make an effective transition to the work force. Such diversity of advanced courses yields a variety of related NSF projects.
W. Robert Stephenson, Dianne Cook, Mark Kaiser, and William Meeker recognized advancements being made in technology and statistical methods and designed a project to incorporate them in advanced statistical methods courses. Through "Beyond Traditional Statistical Methods" (DUE #9751644), they created stand alone modules that are modeled after actual applications in science and engineering, employing real problems and data, that illustrate correct inferential techniques and uses of new statistical methods. Lesson plans, homework assignments, and authentic assessments accompany the modules, which can be used individually or combined to create an entire course. Such a class has been institutionalized at Iowa State University, where the project took place (Duckworth, Stephenson 2002). Like the projects by Siegrist, Rossman, and Chance, this project also has had an impact beyond the university at which it was developed. For example, "Biostatistics: A Second Statistics Course Preparing Undergraduates for Research" (DUE #0410586) by Bessie Kirkwood of Sweet Briar College used the modules to teach Biostatistics and to prepare students for further research in statistics (Kirkwood 2005).
At the University of Minnesota, Twin Cities, Ralph Cook provides an example of projects dealing with applied methods courses. His project "Graphical Paradigms for Teaching and Using Statistics" (DUE #9354678, #9652887) developed a graphical way to teach regression analysis that includes theoretical underpinnings. He produced a textbook, software, and teaching aids to assist educators in teaching regression through graphs, with homework problems that use real data. The software, designed for computing and analyses, is provided freely over the Internet at the project website, http://www.stat.umn.edu/arc/ (Cook 2001).
A joint project from William Swallow, Kimberly Weems, and William Hunt of North Carolina State University (NCSU) and Nagambal Shah and Monica Stephens of Spelman College established a partnership between academia, industry, and government. "Collaborative Research: Training Environmental Statisticians Using Complicated Data Sets to Make Informed Environmental Decisions" (DUE #0229344, #0230471) provided students consulting opportunities with federal, state, and local environmental agencies, offering experience applying technical skills to real problems and the chance to develop communication skills. The partnership aimed to show that NCSU, a large university with a thriving Environmental Statistics program, could help develop such a program at Spelman College, a small liberal arts college (Shah, Stephens 2004).
A common example of an advanced course for statistics majors is a capstone course. Such courses integrate all the experiences, knowledge, and abilities students should have upon graduation. John Spurrier, of the University of South Carolina (USC), developed such a course through his project "A Capstone Course in Statistics" (DUE #9455292). In his course, students work for a hypothetical company and complete assignments that require them to design an experiment, collect and analyze data, and submit oral and written reports, just as they would in a real job. Spurrier published a textbook with eleven capstone experiences that can be integrated individually into existing courses or combined to create a single course. The course at USC has since been institutionalized and is a requirement for graduation for all statistics majors (Spurrier 2001). Daljit Ahluwalia, Bonnie Ray, and Bruce Bukiet completed a similar project at New Jersey Institute of Technology. "Capstone Courses and Projects in Applied Mathematics and Statistics" (DUE #9651404) established a computing laboratory with space for physical experiments and modules in which students learned how mathematics and statistics are used in industry (Ahluwalia, Ray, Bukiet 2000).
One characteristic that makes statistics unique among other disciplines is its wide range of application areas. Many other disciplines, such as business, science, and engineering, use statistics heavily in their research; they are "clients" of statistics. NSF has awarded several projects to educators and researchers outside of the discipline of statistics that address statistical needs of students in these client disciplines.
Engineering is an area of study that is heavily involved in statistics and allows for unique approaches to teach the subject. For instance, "Quality Engineering Laboratory" (DUE #9751244) by Ajit Tamhane and Bruce Ankenman of Northwestern University received funds to establish a laboratory where students actually manufactured staple removers and collected and studied data on the process. Through these handson experiences, students learned control charts, gage repeatability and reproducibility, and experimental design from real, relevant data in the manufacturing context (Tamhane, Ankenman 2001). At the University of Texas, Pan American, Douglas Timmer and Miguel Gonzalez also created an environment where students could be involved in the manufacturing process through "WebBased Active Learning Modules for Teaching Statistical Quality Control" (DUE #0341290). In this project, modules were developed that gave students experience with a virtual manufacturing process and allowed them to analyze real data from a company that networked their data to the university (Timmer, Gonzalez 2004).
Along the lines of the virtual environment, an economics lab was developed that provided simulations of market environments, allowing students to conduct a variety of computerized experiments, through "Experimental Economics Laboratory with Statistical Software" (DUE #9352756) by Gregory Lilly of Elon University. Students had access to faster, easier data collection, data storage, computing, better experimental control, and several statistical packages, including graphics (Lilly 1993). Another project addressing Business Statistics is "Improving Statistical Education through Visualization" (DUE #9554967) by Ronald Tracy, David Doane, and Kieran Mathieson from Oakland University, which produced Visual Statistics 2.0 software and textbook containing 21 learning modules. Each module is designed to engage students in active, selfdiscovery learning through exercises, scenarios, animation, and real data (Tracy, Doane, Mathieson 2001).
"Computer Based Teaching in Epidemiology and Statistics" (DUE #9981001) by Erika Friedmann and Mark Tomita of City University of New York, Brooklyn College teaches epidemiology and statistics using real health data from large national data sets and research databases accessed over the Internet. A computer laboratory was created so that students could analyze this data using SPSS technology. Also, a number of SPSS tutorials were created (Friedmann, Tomita 2002). Utilizing technology and providing experience with real data are critical to statistics education in any discipline, and these projects show that other disciplines agree.
To this point, we have discussed projects according to the level of education they address. We began with math and statistics majors in their first statistics course, followed by those taking advanced courses, and moving on to students learning statistics from client disciplines. However, there are projects that address students at all educational levels. Next we will briefly describe three projects that deal with whole curricula in statistics.
Robert Arnold and Randall Fuller of Colgate University used funds for their project "Computers to Support Quantitative Analysis/Statistics across a Biology Curriculum" (DUE #9851563) to purchase 16 laptop computers for mobile labs that can travel to any classroom. The computers were used to introduce scientific writing and the use of statistics in freshman through junior level courses. Students analyzed their own data using a statistical software package, wrote papers, and designed experiments (Arnold, Fuller 2001). The "Technology Enhanced Core Project" (DUE #9950848) by Edward Reeves and Rebecca Katz reformed eight different classes in the sociology program at Morehead State University. Students in this program learned advanced research skills, bivariate and multivariate inferential statistics, and communication skills through handson learning activities (Reeves, Katz 2002). Another NSFsupported project that crosses curricula, disciplines, and institutions is "CAUSEweb" (DUE # 0333672) directed by Dennis Pearl at The Ohio State University. CAUSEweb, run by the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE), is an ever growing digital library of over 1,000 different instructional materials for statistics education, found at http://www.causeweb.org. The materials in this library cover high school through graduate levels of education, approximately 20 different application areas, and numerous statistical topics.(Green, McDaniel, Rowell 2005).
Other projects that reach farther than specified groups of students are NSF’s funded projects for faculty enhancement. Some of these projects in statistics are intended to help faculty who teach statistics but may not have a background in the subject. The Mathematical Association of America (MAA) received funding from NSF through its grants "MAA: Comprehensive Professional Development Program for Mathematics Faculty" (DUE #0089005) and "Professional Enhancement Program (PREP)" (DUE #0341481). These projects sponsored 49 workshops between 2001 and 2005 for mathematics faculty across the nation to enhance their teaching skills. Four of these workshops dealt specifically with statistics education, 2 addressing introductory statistics, and 2 addressing advanced statistics (Pearson 2004). The PRESTAT workshops (DUE #9752749) organized by Mike Perry of Appalachian State University provided a similar opportunity for college faculty who teach preservice and inservice teachers. The goal was to help math educators implement an effective statistics curriculum and to develop guidelines and activities for such a curriculum (NSF 1998c).
Because faculty teaching statistics in other disciplines most likely do not have formal statistical training, enhancement opportunities are critical to improve teaching. J. Theodore Anagnoson of California State, Los Angeles received funds for his project "Two One Week Workshops for Social Science Faculty on Exploratory Data Analysis Using Microcomputers" (DUE #9255461). Twenty social scientists with varying levels of statistical background were admitted to weeklong workshops covering exploratory data analysis techniques using the statistical package Stata. The goal was to provide faculty with alternatives to traditional statistical instruction methods (Anagnoson 1993).
Roxy Peck’s innovative workshops, "Improving Statistical Education: Faculty Enhancement through Collaboration" (DUE #9455055), paired college statistics faculty with industrial statisticians to discuss needed improvements in statistical education. The pairs made onsite visits to each environment, each learning the conditions of the other to better understand how to meet educational needs. They also created case studies based on actual practices to be used in statistics courses and explored ways the two groups can support each other through open communication and collaboration. Her project allowed students to experience authentic applications and data in context (Peck 1995).
In this review of a decade of NSF DUE funding which affects undergraduate statistics education, we describe characteristics of 25 projects that focus on statistics majors and minors in both statistics/mathematics departments as well as in other client disciplines. Through this process, pedagogical themes emerged particularly related to course instruction to enhance the teaching of statistics for a variety of courselevels: introductory postcalculus statistics courses, advanced statistics courses, and advanced statistics in client disciplines. These themes included using real data and real applications, integrating technology effectively by providing experience with statistical computing and simulation, involving students in real consulting type environments as one of several ways of developing and improving communication skills, and promoting understanding of statistical theory through the application of mathematical background to carefully constructed statistics lessons in appropriate classes. It is apparent that NSF has been extremely involved in the improvement of undergraduate statistics education, and the projects that have been sponsored are aligned with recommendations from USEI and ASA.
Award Number 
Title 
Principal Investigator 
Start Date 
NSF Program 
Award Amount 
Institution 
0101686 
Lexington Collaborative for Revitalizing and Improving Middle
Mathematics (LCRIMM) 
Lillie Crowley 
July 1, 2001 
CETP 
$300,000 

9350693 
Renovation of the
Undergraduate Statistics Curriculum 
Roxy Peck 
June 1, 1993 
ILI 
$30,000 

9351541 
Statistics Electronic
Classroom (SEC) for Instruction in Quantitative Methods in Psychology 
David J. Weiss 
July 1, 1993 
ILI 
$28,192 

9352756 
Experimental Economics
Laboratory with Statistical Software 
Gregory A. Lilly 
August 1, 1993 
ILI 
$26,697 

9350819 
Improving Undergraduate
Instruction in Statistics in the Social Sciences 
James F. O'Connor 
September 1, 1993 
ILI 
$23,495 
Eastern Kentucky
Univ. 
9450998 
A Laboratory for Numerical
Computation 
Robert Pervine 
June 1, 1994 
ILI 
$27,197 
Murray State Univ. 
9551659 
Implementing an Interactive
Computer Laboratory to Support DiscoveryBased Statistics Courses for Liberal
Arts Students and Future Teachers 
Anne D. Sevin 
June 1, 1995 
ILI 
$29,941 

9551745 
Computerizing the Research
and Statistical Training of Undergraduate Psychology Students 
Mark Johnson 
June 1, 1995 
ILI 
$23,221 

9551184 
Undergraduate Statistical
Laboratory 
Anthony A. Salvia 
September 1, 1995 
ILI 
$20,942 

9551942 
Mobile Psychology Lab for 
James Towey 
September 1, 1995 
ILI 
$30,871 

9651404 
Capstone Courses &
Projects in Applied Mathematics & Statistics 
Daljit S. Ahluwalia 
July 1, 1996 
ILI 
$28,724 
New Jersey Institute for Technology 
9651091 
Visualizing and Writing
Mathematics 
James Callahan 
July 1, 1996 
ILI 
$49,067 

9651276 
The Development of an
Undergraduate Psychology Computer Laboratory 
Janet Kottke 
July 1, 1996 
ILI 
$60,000 
California State Univ., 
9751644 
Beyond Traditional
Statistical Methods 
W. Robert Stephenson 
July 1, 1997 
ILI 
$57,000 
Iowa State Univ. 
9751407 
A Computer Laboratory for
Mathematics Instruction 
John Buoni 
August 1, 1997 
ILI 
$41,118 
Youngstown State Univ. 
9751114 
Development of an Intranet
to Enhance the Instruction of Research Methodology in Psychology 
John Govern 
September 1, 1997 
ILI 
$16,530 

9751244 
Quality Engineering
Laboratory 
Ajit Tamhane 
September 1, 1997 
ILI 
$28,367 
Northwestern Univ. 
9851563 
Computers to Support
Quantitative Analysis/Statistics Across a Biology Curriculum 
Robert M. Arnold 
June 1, 1998 
ILI 
$21,799 

9851492 
Infusing Technology into
the Psychology Curriculum: A Model Laboratory to Promote Scientific Thinking 
William Lammers 
September 1, 1998 
ILI 
$22,621 

9354678 
Graphical Paradigms for
Teaching and Using Statistics 
Ralph D. Cook 
December 1, 1993 
CCD 
$204,922 
UMN, Twin
Cities 
9455292 
A Capstone Course in
Statistics 
John D. Spurrier 
June 1, 1995 
CCD 
$29,960 
USC, Columbia 
9554967 
Improving Statistical
Education through Visualization 
Ronald L. Tracy 
June 1, 1996 
CCD 
$99,951 

9555178 
A Course in Computer
Intensive Statistical Methods for Mathematical Sciences Students 
Jenny A. Baglivo 
July 1, 1996 
CCD 
$102,310 

9652870 
Virtual Laboratories in
Statistics 
Kyle Siegrist 
January 1, 1997 
CCD 
$110,542 
U. 
9652887 
Graphical Paradigms for
Teaching and Using Statistics 
Ralph D. Cook 
July 1, 1997 
CCD 
$124,657 
UMN, Twin
Cities 
9752622 
A New Course in Statistical
Process Control Integrating an Industrial Production Facility as the OnLine
Laboratory 
Ronald W. Garrett 
February 1, 1998 
CCD 
$89,083 
Grand Valley S.U. 
9752058 
Computer Simulations of
Industrial Statistical Application for Undergraduates and Technicians 
David Shellabarger 
October 1, 1997 
ATE 
$262,800 
Lane CC 
9950476 
A DataOriented, Active
Learning, PostCalculus Introductions to Statistical Concepts, Methods, and
Theory 
Allan J. Rossman 
June 1, 1999 
EMD 
$252,828 

0089377 
The Probability/Statistics
Object Library 
Kyle Siegrist 
January 1, 2001 
EMD 
$158,755 
U. 
0089004 
Statistical Applications
for the Mathematics Curriculum 
George Cobb 
January 1, 2001 
EMD 
$149,927 

0127398 
Transforming Biological and
Engineering Statistics at >Penn
State 
William L. Harkness 
February 1, 2002 
EMD 
$75,000 

0321973 
A DataOriented, Active
Learning, PostCalculus Introductions to Statistical Concepts, Methods, and
Theory 
Allan J. Rossman 
April 10, 2002 
EMD 
$142,500 

0127557 
Broadening the Scope of
Statistical Education through Technology 
Deborah A. Nolan 
May 15, 2002 
EMD 
$350,000 

0230803 
Stem and Tendril:
Vertically Integrated Statistics Laboratories 
Andrew Poje 
January 15, 2003 
EMD 
$74,836 
CUNY, Staten Island 
0229344 
Collaborative Research:
Training Environmental Statisticians Using Complicated Data Sets to Make More
Informed Environmental Decisions 
Nagambal D. Shah 
February 15, 2003 
EMD 
$246,137 

0230471 
Collaborative Research:
Training Environmental Statisticians Using Complicated Data Sets to Make More
Informed Environmental Decisions 
William Swallow 
February 15, 2003 
EMD 
$247,249 

0231322 
Conceptual Statistics:
Engaging Students in Statistical Discovery 
W. Robert Stephenson 
May 15, 2003 
EMD 
$74,976 
Iowa State Univ. 
0341157 
Integration of
Probabilistic and Statistical Design Methods into Engineering Design Courses 
Xiaoping Du 
February 15, 2004 
EMD 
$67,994 
Univ. MissouriRolla 
0341290 
Webbased Active Learning
Modules for Teaching Statistical Quality Control 
Douglas Timmer 
March 1, 2004 
EMD 
$74,907 

9950848 
Technology Enhanced Core
Project 
Edward Reeves 
August 1, 1999 
AI 
$89,177 
Morehead State Univ. 
9952508 
Computer Laboratory for
Undergraduate Research Courses in Behavioral Sciences 
Jarl Ahlkvist 
May 15, 2000 
AI 
$32,952 

9981001 
Computer Based Teaching in
Epidemiology and Statistics 
Erika Friedmann 
June 1, 2000 
AI 
$99,993 
CUNY, Brooklyn 
0126401 
Collaborative Research:
Adaptation and Implementation of Activity and WebBased Materials into
PostCalculus Introductory Probability and Statistics Courses 
Tracy GoodsonEspy 
June 1, 2002 
AI 
$36,886 
U. 
0126600 
Collaborative Research:
Adaptation and Implementation of Activity and WebBased Materials into
PostCalculus Introductory Probability and Statistics Courses 
Ginger H. Rowell 
June 1, 2002 
AI 
$33,939 
Middle Tennessee State Univ. 
0126716 
Collaborative Research:
Adaptation and Implementation of Activity and WebBased Materials into
PostCalculus Introductory Probability and Statistics Courses 
Myrtis L. Lunsford 
June 1, 2002 
AI 
$25,433 
Athens State Univ. 
0350724 
Collaborative Research:
Adaptation and Implementation of Activity and WebBased Materials into
PostCalculus Introductory Probability and Statistics Courses 
Myrtis L. Lunsford 
July 11, 2003 
AI 
$17,490 
U. 
0410586 
Biostatistics: A Second
Statistics Course Preparing Undergraduates for Research 
Bessie Kirkwood 
July 15, 2004 
AI 
$35,974 

0089005 
MAA Comprehensive
Professional Development Program For Mathematics Faculty 
J Michael Person 
April 1, 2001 
ND 
$966,291 
MAA 
0341481 
Professional Enhancement
Program (PREP) 
J Michael Pearson 
February 1, 2004 
ND/NSDL 
$462,690 
MAA 
9255461 
Two One Week Workshops for
Social Science Faculty on Exploratory Data Analysis Using Microcomputers 
J. Theodore Anagnoson 
April 1, 1993 
UFE 
$76,642 
California State LA Univ. 
9455055 
Improving Statistics
Education: Faculty Enhancement Through Collaboration with Industry 
Roxy Peck 
January 1, 1995 
UFE 
$121,775 

9752749 
PRESTAT Project 
Mike Perry 
May 1, 1998 
UFE 
$59,992 
Appalachian S.U. 
0333672 
CAUSEweb: A Digital Library
of Undergraduate Statistics Education 
Dennis Pearl 
October 1, 2003 
NSDL 
$824,945 
Ohio State Univ. 
Megan R. Hall worked on this project as an undergraduate student at Middle Tennessee State University. This work was supported by Middle Tennessee State University, Graduate Studies, Undergraduate Research, Scholarship, and Creativity Award.
Ahluwalia, D., Ray, B.K., and Bukiet, B.G.(2000), "Capstone Courses and Projects in Applied Mathematics and Statistics #9651404," DUE PIRS Search Engine.Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9651404.
American Statistical Association. (2002), Curriculum Guidelines for Undergraduate Programs in Statistical Science. Retrieved March 2, 2005 from http://www.amstat.org/education/index.cfm?fuseaction=Curriculum_Guidelines.
Anagnoson, J.T.(1993), "Two One Week Workshops for Social Science Faculty on Exploratory Data Analysis Using Microcomputers Abstract #9255461."Retrieved March 2, 2005 from NSF Online Database http://www.nsf.gov/awardsearch/.
Arnold, R.M., and Fuller, R.L. (2001), "Computers to Support Quantitative Analysis/Statistics Across a Biology Curriculum #9851563," DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9851563.
Baglivo, J. (2000), "A Course in Computer Intensive Statistical Methods for Mathematical Sciences Students #9555178," DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9555178.
Bryce, G. R.(2002), "Undergraduate Statistics Education: An Introduction and Review of Selected Literature," Journal of Statistics Education, v10, n2.Retrieved June 10, 2005 from http://www.amstat.org/publications/jse/v10n2/bryce.html.
Bryce, G. R., Gould, R., Notz, W.I., and Peck, R.L. (2001), "Curriculum Guidelines for Bachelor of Science Degrees in Statistical Science," The American Statistician, 55, 713.
Cobb, G.W. (1993), "Reconsidering Statistics Education: A National Science Foundation Conference," Journal of Statistics Education, v1, n1.Retrieved March 2, 2003 from http://www.amstat.org/publications/jse/v1n1/cobb.html.
Cook, R.D. (2001), "Graphical Paradigms for Teaching and Using Statistics #9652887," DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9652887.
Duckworth, W.M., and Stephenson, W.R.(2002), "Beyond Traditional Statistical Methods," The American Statistician, 56, 230233.
Friedmann, E., and Tomita, M. (2002), "Computer Based Teaching in Epidemiology and Statistics #9981001," DUE PIRS Search Engine.Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9981001.
Green, L.B., McDaniel, S.N., and Rowell, G.H. (2005), "Online Statistics Resources Across Disciplines," Journal of Online Learning and Teaching, pending publication.
Higgins, J.J. (1999), "Nonmathematical statistics: a new direction for the undergraduate discipline," The American Statistician, 53, 16.
Kirkwood, B. (2005), "Biostatistics: A Second Statistics Course Preparing Undergraduates for Research #0410586," DUE PIRS Search Engine. Retrieved July 20, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=0410586.
Lilly, G. (1993), "Experimental Economics Laboratory with Statistical Software Abstract #9352756." Retrieved March 2, 2003 from NSF Online Database http://www.nsf.gov/awardsearch/.
Lunsford, M.L.(2004), "Collaborative Research: Adaptation and Implementation of Activity and WebBased Materials into PostCalculus Introductory Probability and Statistics Courses #0350724," DUE PIRS Search Engine.Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=0350724.
Marder, C., McCullough, J., and Perakis, S.(2001), "Evaluation the National Science Foundation’s Undergraduate Faculty Enhancement Program. "Retrieved March 21, 2005 from http://www.nsf.gov/pubs/2001/nsf01123/nsf01123_1.pdf.
Marquardt, D.W. (1987), "The Importance of Statisticians," Journal of the American Statistical Association, 82, 397, 17.
Minton, P.D. (1983), "The Visibility of Statistics as a Discipline," The American Statistician, 37, 284289.
National Science Foundation. (1995), "Division of Undergraduate Education Program Announcement and Guidelines," nsf9610, NSF EHR DUE. Retrieved September 2, 2005 from http://www.nsf.gov/pubs/stis1995/nsf9610/nsf9610.txt
National Science Foundation. (1996a), "The Learning Curve: What We Are Discovering About U.S. Science and Mathematics Education," Report nsf9653, National Science Foundation, Directorate for Education and Human Resources, ed. Larry E. Suter.Retrieved May 12, 2005 from http://www.nsf.gov/pubs/1996/nsf9653/nsf9653.htm.
National Science Foundation. (1996b), "Shaping the Future: New Expectations for Undergraduate Education in Science, Mathematics, Engineering, and Technology," Report nsf96139, National Science Foundation, Directorate for Education and Human Resources.Retrieved May 12, 2005 from http://www.nsf.gov/pubs/stis1996/nsf96139/nsf96139.txt.
National Science Foundation. (1998a), "A Report on an Evaluation of the National Science Foundation’s Instrumentation and Laboratory Improvement Program," Report nsf9833, National Science Foundation Division of Undergraduate Education. Retrieved May 26, 2005 from http://www.nsf.gov/pubs/1998/nsf9833/ilitoc.htm
National Science Foundation. (1998b), "Undergraduate Education Science, Mathematics, Engineering, Technology Program Announcement and Guidelines," nsf9845, NSF EHR DUE. Retrieved May 12, 2005 from http://www.nsf.gov/pubs/1998/nsf9845/nsf9845.txt
National Science Foundation. (1998c), "Directory of NSFSupported Undergraduate Faculty Enhancement Projects." Retrieved May 26, 2005 from http://www.nsf.gov/pubs/1998/nsf98108/.
National Science Foundation.(1999) "Collaboratives for Excellence in Teacher Preparation (CETP) Program Announcement and Guidelines," nsf9953, NSF EHR DUE. Retrieved May 12, 2005 from http://www.nsf.gov/pubs/1999/nsf9953/nsf9953FileE.htm
National Science Foundation. (2003a), "Course, Curriculum, and Laboratory Improvement (CCLI) Educational Materials Development (EMD) and National Dissemination (ND) Tracks Program Solicitation," nsf03558, NSF EHR DUE.Retrieved May 12, 2005 from http://www.nsf.gov/pubs/2003/nsf03558/nsf03558.htm.
National Science Foundation. (2003b), "Course, Curriculum, and Laboratory Improvement (CCLI) Assessment of Student Achievement (ASA) Track Program Solicitation," nsf03584, NSF EHR DUE. Retrieved May 12, 2005 from http://www.nsf.gov/pubs/2003/nsf03584/nsf03584.htm.
National Science Foundation. (2003c), "Course, Curriculum, and Laboratory Improvement (CCLI) Adaptation & Implementation (A&I) Track Program Solicitation," nsf03598, NSF EHR DUE. Retrieved May 12, 2005 from http://www.nsf.gov/pubs/2003/nsf03598/nsf03598.htm.
National Science Foundation. (2005a), "Course, Curriculum, and Laboratory Improvement (CCLI) Program Solicitation," nsf05559, NSF EHR DUE.Retrieved May 12, 2005 from http://www.nsf.gov/pubs/2005/nsf05559/nsf05559.htm.
National Science Foundation. (2005b), "National Science, Technology, Engineering, and Mathematics Education Digital Library (NSDL) Program Solicitation," nsf05545, NSF EHR DUE. Retrieved May 12, 2005 from http://www.nsf.gov/pubs/2005/nsf05545/nsf05545.htm.
Nolan, D., and Lang, T. (2003), "Case Studies and Computing: Broadening the Scope of Statistical Education," in Proceedings of the 2003 ISI Meeting.Retrieved March 2, 2005 from http://www.stat.berkeley.edu/~nolan/Papers/isi03.pdf.
Peck, R. (1995), "Improving Statistics Education: Faculty Enhancement Through Collaboration with Industry Abstract # 9455055." Retrieved March 2, 2003 from NSF Online Database http://www.nsf.gov/awardsearch/.
Pearson, J.M. (2004), "PRofessional Enhancement Program (PREP) #0341481," DUE PIRS Search Engine.Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=0341481.
Reeves, E., and Katz, R.(2002), "Technology Enhanced Core Project #9950848," DUE PIRS Search Engine.Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9950848.
Rossman, A., and Chance, B. (2004), "A DataOriented, Active Learning, PostCalculus Introduction to Statistical Concepts, Applications, and Theory," presented at the IASE 2004 Roundtable on Curricular Development in Statistics Education, Lund, Sweden, June 28 – July 3, 2004. Retrieved June 6, 2005 from http://www.rossmanchance.com/iscat/RossmanChanceIASEpaper.pdf.
Shah, N., and Stephens, M. (2004), "Collaborative Research: Training Environmental Statisticians Using Complicated Data Sets to Make More Informed Environmental Decisions #0229344," DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=0229344.
Siegrist, K. (2000), "Virtual Laboratories in Statistics #9652870," DUE PIRS Search Engine.Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9652870.
Siegrist, K. (2005), "The Probability/Statistics Object Library #0089377," DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=0089377.
Snee, R.D. (1993), "What’s Missing in Statistical Education?" The American Statistician, 47, 149154.
Spurrier, John.(2001), "A Capstone Course for Undergraduate Statistics Majors," Journal of Statistics Education, v9, n1. Retrieved June 10, 2005 from http://www.amstat.org/publications/jse/v9n1/spurrier.html.
Tamhane, A., and Ankenman, B.(2001), "Quality Engineering Laboratory #9751244," DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9751244.
Timmer, D., and Gonzalez, M.(2004), "WebBased Active Learning Modules for Teaching Statistical Quality Control Abstract #0341290. "Retrieved March 2, 2003 from NSF Online Database http://www.nsf.gov/awardsearch/.
Tracy, R.L., Doane, D.P., and Mathieson, K.R.(2001), "Improving Statistical Education Through Visualization #9554967," DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9554967.
Megan R. Hall
Middle Tennessee State University
Murfreesboro, TN 37132
Todd101215@aol.com
Ginger Holmes Rowell, Ph.D.
Middle Tennessee State University
Murfreesboro, TN 37132
rowell@mtsu.edu
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