Megan R. Hall and Ginger Holmes Rowell
Middle Tennessee State University
Journal of Statistics Education Volume 16, Number 2 (2008), jse.amstat.org/v16n2/rowell1.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: Introductory Statistics; Curriculum Guidelines; Teaching Materials; Grant Projects.
This paper describes 27 National Science Foundation supported grant projects that have innovations designed to improve teaching and learning in introductory statistics courses. The characteristics of these projects are compared with the six recommendations given in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2005 for teaching an introductory course in statistics. Through this analysis, we are able to see how NSFsupported introductory statistics education projects during the last decade achieve the GAISE ideals. Thus, materials developed from many of these projects provide resources for first steps in implementing GAISE recommendations.
For many years, statistics educators have been concerned with reforming undergraduate education, especially the introductory course in statistics. Throughout this article, introductory statistics refers to Joan Garfield’s definition: the "noncalculus based, often terminal, introductory applied statistics course" for students not majoring in the subject (Garfield 2000, p.2). The latest efforts to address this first course have evolved for some time and have resulted in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report (2005), which sets forth six recommendations for teaching the introductory course. Likewise, the National Science Foundation (NSF) has addressed undergraduate statistics education by funding many projects aligned with statistics education standards; George Cobb (1993) reviewed 12 such projects, lending inspiration to this report. Following Cobb’s lead, this article reviews the GAISE recommendations and supporting literature, describes the NSF programs used to support this reform, and examines 27 NSF projects, funded from 1993 to 2004, which address the introductory statistics course. By comparing these projects with the GAISE recommendations, we are able to show how NSF has been supporting GAISE principles over the past decade.
As statistics has made its way into the undergraduate curriculum over the past century, the introductory course has undergone numerous changes. Always at the forefront of reform is the effort to improve teaching and student learning in this course (GAISE 2005). In the early 1990’s, George Cobb organized a focus group to set up guidelines for teaching this course. This group produced a paper called "Teaching Statistics" that set forth three recommendations: 1) emphasize statistical thinking; 2) more data and concepts, less theory and fewer recipes; and 3) foster active learning (Cobb 1992). Toward the end of the decade, the launching of the Undergraduate Statistics Education Initiative (USEI) drew more attention to the introductory course through a paper calling for increased attention to statistical thinking. That article also reported that teachers of statistics were already increasing their use of technology and active learning in the classroom (Garfield, Hogg, Schau, Whittinghill 2002). These publications are just two examples of the work done in statistics education reform that has helped lead to the production of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report (2005).
The GAISE College Report (2005) was developed by a group of statisticians/statistics educators with funding from the American Statistical Association (ASA). On May 17, 2005, the ASA approved this document which provides six primary recommendations for teaching introductory statistics: emphasize statistical literacy and develop statistical thinking, use real data, stress conceptual understanding rather than mere knowledge of procedures, foster active learning in the classroom, use technology for developing conceptual understanding and analyzing data, and use assessments to improve and evaluate student learning.
The common thread throughout introductory statistics education reform efforts is the emphasis on statistical thinking and literacy (Cobb 1992, Snee 1993, Garfield et al 2002, MAA 2004). Instructors of introductory level courses want their students to understand statistical terms, symbols, graphs, and fundamental ideas, which the GAISE authors consider to be statistical literacy. Along with literacy, students in these courses should be able to think statistically, meaning they should understand the need for data, the importance of data production, the omnipresence of variability, and the quantification and explanation of variability (GAISE 2005). Rumsey (2002) adds to this definition the ability to make informed decisions, while Chance (2002) wants her students to see the big picture and think of statistics in terms of the whole process, rather than isolated techniques. Furthermore, since statistics are present everywhere in the media it is important for citizens to be able think critically about the information thrust upon them (Rumsey 2002; Sullivan 1993).
Because statistics is about understanding data (Hakeem 2001), students should have access to and experience with real data. The use of real data in introductory statistics courses provides authenticity, helps address issues of data production and collection, gives reallife context to a problem, and can increase student interest in the course (GAISE 2005). There are three kinds of data which accomplish these goals, each with advantages and disadvantages. Class generated data can provide meaningful connections for students because they participated in its production; unfortunately, it can also be toylike and shallow. Archival data gives students experience with realworld statistics and can be complex and rich in nature; however, students’ excitement may be compromised since they were excluded from the production process, and variability can be hidden. Simulated data emphasizes variability well and allows the instructor more control, but it is not real (Cobb 1993). Regardless of the type, real data used in context can motivate and engage students in the statistical process without being burdensome thanks to technological advances (GAISE 2005).
Like Cobb (1992), the GAISE authors believe that topical coverage can be sacrificed for conceptual understanding and suggest paring down the course syllabus. "If students don’t understand the important concepts, there’s little value in knowing a set of procedures," (GAISE 2005, p. 10). Experts in other disciplines, such as biology professors Udovic, Morris, Dickman, Postlethwait, and Wetherwax (2002), agree that deep understanding of fewer concepts is better than shallow understanding of many. This has been a principle part of the undergraduate statistics education reform movement, as more instructors are focusing on concepts in their courses (Garfield 2000). The Mathematical Association of America (2004) also endorsed this recommendation for statistics courses in their Committee on the Undergraduate Program in Mathematics 2004 Curriculum Guidelines.
There are many advantages to incorporating active learning in the introductory statistics course. It allows students to discover concepts, engage in the statistical process, communicate in statistical language, and work in teams, as well as provides instructors informal methods of assessment (GAISE 2005). Active learning ideas can be traced back to Socrates and are found in the work of Dewey, Piaget, and Lewin (Zeichner, Litcher 1998). In a study of student learning styles in English, chemistry, mathematics, and psychology courses, August, Hurtado, Wimsatt, and Dey (2002) found that 91% of students felt they learned better from inclass activities and 85% found lectureonly classes boring. McConnell, Steer, and Owens (2003) found that active learning techniques in geology courses increase student participation and that students who have engaged in active learning perform better on exams and logical thinking tests than those in traditional settings. To foster active learning, activities should focus on conceptual understanding and discovery learning and can be group work, laboratory activities, or class discussions (GAISE 2005).
Technology, a resource which has been transforming statistical research for many years, has also been a major component of statistics education reform (Moore, Cobb, Garfield, Meeker 1995, Garfield 2000). This remarkable tool can be used in the introductory statistics course to analyze data, simulate concepts, or provide alternative assessments, while motivating and exciting students (GAISE 2005, SchenoneStevens 1999). However, there are some cautions teachers should heed when exploiting technology’s benefits. When used for its own sake, technology has no redeeming educational value (SchenoneStevens 1999); rather than replace human interaction, technology should enrich teaching styles and techniques (Moore, Cobb, Garfield, Meeker 1995). When used correctly, technology can greatly enhance student learning (GAISE 2005).
It is understood that students are concerned with how they are assessed; therefore, assessment techniques should place value on learning objectives and understanding key ideas (GAISE 2005). High quality assessments should be aligned with national standards, should measure what matters for improvement, and should be learning experiences themselves (Cobb 1993; Caudell 1996; Chance and Garfield 2002). Such instruments drive curriculum, reflect student learning, and have reallife context (Caudell 1996). Examples include formative evaluations, written reports, portfolios, experiments, essays, speeches, projects, and activities (Caudell 1996, McConnell, et al 2003, GAISE 2005).
These recommendations made by the GAISE authors do not stand alone. Each can be met by materials or techniques intended to address another. For instance, a teacher may implement technology in order to convey a particular concept, an activity may involve student collection of data, or a group project may be used for assessment. Furthermore, all of these can move students toward being statistically literate or employing statistical thinking. In any case, statistics educators need not approach these guidelines as six separate techniques to master and implement, but as one complete way to help students become good statistical citizens in an information age.
These approaches to teaching are not limited to introductory statistics or any statistics course. The ideas of scientific reasoning, active learning, conceptual understanding, use of technology, and appropriate assessments are present in biology, geology, business, engineering psychology, and other disciplines that depend on statistics (RolkerDolinsk, Qualters 1994; Craddock 1998; Bass, Rosenzwig 1999; Hakeem 2001; McConnell, et al 2003; McCormick, MacKinnon, Jones 1999; Udovic, et al 2002). Some National Science Foundation (NSF) projects described in this paper meet GAISE recommendations by addressing introductory level statistics through courses in these disciplines.
The NSF has supported undergraduate education since its inception in 1950. In order to take a more central role in reform efforts, NSF established the Division of Undergraduate Education (DUE) (NSF 1996). The following programs are or were supported by DUE and have had an impact on introductory statistics courses.
The Instrumentation and Laboratory Improvement (ILI) program of NSF DUE began in 1988 in order to encourage and support improvement in laboratory curricula for science, technology, engineering, and mathematics (STEM) education institutionally and nationwide. Projects funded through this program helped create and equip laboratory facilities, upgrade equipment for laboratory instruction, develop laboratory exercises that demonstrate basic principles, and stimulate interest in STEM courses by making them relevant and understandable. The program accepted its final proposals in fiscal year 1998 and transitioned into the Course, Curriculum, and Laboratory Improvement (CCLI) program (NSF 1998).
The main objectives of the Course and Curriculum Development (CCD) program, which ran from 1988 until 1998, were to improve undergraduate STEM teaching, increase student understanding of and attitudes toward STEM, and to place greater value on teaching and scholarship through the development and adaptation of courses, curriculum, and educational materials (Eiseman, Fairweather, Rosenblum, Britton 1998). Grants funded through this program often produced textbooks, manuals, or course materials or created courses or sequences of courses. Like ILI, CCD was finally assimilated into CCLI (NSF 1998).
Established in 1998, the Course, Curriculum and Laboratory Improvement (CCLI) program combined properties of CCD and ILI, funding proposals for curricular development and purchase of instructional laboratory equipment. The initial four tracks of CCLI were intended to stimulate creative teaching and pedagogical scholarship among faculty (NSF 1998). The Educational Materials Development (EMD) track aimed to encourage and support the development of quality instructional materials that enhance student learning in STEM, while the Adaptation and Implementation (AI) track assisted in integrating exemplary materials, laboratory experiences, and educational practices at other diverse universities (NSF 2003a, NSF 2003b). By sponsoring faculty development opportunities, the National Dissemination (ND) track of CCLI promoted the introduction of exemplary materials, practices, and techniques to large numbers of colleges and universities nationwide (2003a). Finally, the Assessment of Student Achievement (ASA) track developed effective assessment tools associated with student learning in STEM and supported the adaptation, implementation, and dissemination of such tools (NSF 2003c). These four tracks were phased out in 2006 to make 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).
Another program that was integrated into CCLI was the Undergraduate Faculty Enhancement (UFE) program, which operated from 1988 to 1998. UFE sought to provide faculty with opportunities to experience new and exciting developments in undergraduate education such as new content, teaching methods, experimental techniques, and technology. Funded projects conducted workshops, short courses, seminars, and other such activities to promote these developments. The program supported more than 500 projects and over 750 workshops during its lifetime (Marder, McCullough, Perakis 2001). Another program for educators was the Collaboratives for Excellence in Teacher Preparation (CETP) program founded in 1993. The goal was to increase the number and quality of future preKindergarten through 12^{th} grade teachers, emphasizing subject area competence, effective pedagogical techniques, and national standards for math and science. This program was redesigned, and from 2003 to 2005 was the Teacher Professional Continuum (TPC) program ( NSF 1999; Prival 2008b). Currently, components of this program are interwoven with the Discovery Research K12 (DRK12). (Prival 2008a)
The National Science, Technology, Engineering, and Mathematics Digital Library (NSDL) program supports the collection and organization of educational materials into a national online digital library through projects that develop and enhance collections as well as implement digital library services. Projects can support existing resource providers, maintain material currency and selection criteria, select existing materials for inclusion, or fund workshops promoting the library (NSF 2005b).
As noted above, any given educational technique, practice, or set of materials need not be isolated to one specific GAISE recommendation. Often, by setting out to meet one recommendation, educators end up meeting several at a time. By searching NSF's Award Search Webpage (www.nsf.gov), we were able to find 110 projects affecting introductory statistics funded between 1993 and 2004. Of these, 95% met at least one GAISE recommendation, while 65% met more than one. The NSF funded projects that follow are described in terms of one GAISE recommendation, but that does not mean they meet only that recommendation. Projects were selected to exemplify the qualities of a particular recommendation, even if they meet more than one, as many do. Furthermore, we do not claim that this list is exhaustive of NSF projects that meet GAISE guidelines. If you participated in an NSF project that fits the nature and scope of this article and is not discussed or listed in the Appendix, we extend our apologies for the omission.
Projects that address the first guideline are targeted at helping students become statistically literate, critical thinkers, and informed statistical citizens. The Electronic Encyclopedia of Statistical Examples and Exercises (EESEE; http://www.whfreeman.com/eesee/eesee.html) and the Data and Story Library (DASL: http://lib.stat.cmu.edu/DASL/) are online resources full of real datasets, case studies, and other materials for use in statistics classes. In order to enhance these two resources, project investigators Paul Velleman, William Notz, Elizabeth Stasny, and Dennis Pearl led "Interactive Video Resources for Learning Statistics" (#9555073, #9555233). This project added video resources of current events found on the news and other television programs to help students think critically about statistical applications in real world events (Notz, Pearl, Stasny 1996).
Another project focused on current events that utilizes the EESEE and DASL, is "Change: Current Studies of Current Chance Issues, Phase II," (#9354592) by Laurie Snell. Chance is a quantitative literacy course designed to turn students into informed critical readers by basing the course on statistical concepts found in current events. Students read articles from Chance Magazine and other journals, critiquing the statistical methods used. Topics covered include probability concepts, descriptive statistics, design of experiments, sampling, correlation, and exploratory data analysis. Online and printed materials are available to help instructors teach such a course (Snell 1994, http://www.dartmouth.edu/~chance/). Additionally, a workshop called "Chance Workshop" (#9653416) was conducted to teach educators how to use current events to teach probability and statistics concepts (Snell 1997).
Other workshops have been conducted to help teachers with little or no statistical training learn better ways to teach statistical literacy. Two examples are George Cobb and Mary Parker’s "Statistical Thinking and Teaching Statistics"(#9255447) and its successor "STATS: Statistical Thinking with Active Teaching Strategies," (#9554621) led by Allan Rossman and Thomas Short. These projects supported numerous weeklong workshops emphasizing statistical thinking through real data, conceptual understanding, active learning, software, and assessments, thus touching all components of GAISE (Cobb and Parker 1998; Rossman and Short 1999).
Students in other disciplines also benefit from statistical literacy and thinking skills. To help biology students appreciate statistics and improve their quantitative reasoning skills, "IBASE: Integrating Biology and Statistics Education," (#0309751) by James Watrous, Deborah Lurie, and Denise Ratterman, created two courses, biology and statistics, to be taken simultaneously. In the biology course, students collected data from experiments completed in the lab and then analyzed the data in the statistics course. Thus the students were able to learn realworld applications in a relevant situation, reducing anxiety toward statistics and providing better understanding (Watrous, Lurie, Ratterman 2003).
Real data is often easiest to collect in other disciplines that use statistics on a regular basis. For example, the American Sociological Association, partnering with the University of Michigan, sponsored "Collaborative Project on Integrating Census Data Analysis into the Curriculum" (#0088715, #0089006) led by William Frey, Carla Howery and Felice Levine. This National Dissemination project attempted to revise the sociology curriculum at numerous schools by emphasizing the use of real data from the US Census Bureau. The project investigators called for proposals from other universities to take part in this project in order to have widespread impact (Frey 2001, American Sociological Association 2002). The natural sciences are another area with easy access to real data. "Service Learning in Chemistry: Lead in Soil from Vehicle Emissions" (#0410115) by Hal Van Ryswyk incorporated data analysis into introductory chemistry classes. Students sampled and tested soil for lead, analyzed their own data, and prepared written and oral presentations. The students in these courses also collaborated with students in probability and statistics courses as well as local elementary schools (Van Ryswyk 2004). For those educators without such easy access to real data, there are other ways to find it. For example, James Albert designed an introductory statistics course based entirely on baseball statistics through "Development of Sports Statistics Modules for Introductory Statistics Classes" (#0088703). Data came from baseball cards, the Internet, and simulation. Students were able to understand concepts and analyze real data in an interesting context (Albert 2002).
Analyzing data can be difficult without computer access and appropriate software. Robert Gould and Mahtash Esfandiari’s goal for "A Statistics Undergraduate Computing Laboratory" (#9981172), funded in 2000, was to establish a computer laboratory for statistics courses, where students could analyze real data, teaching the value of statistical thinking and deeper intuitive understanding of the entire data analysis process. The project investigators believe real datasets can help students confront important basic problems in statistics without the datasets being huge and messy (Gould, Esfandiari 2003).
In order to place a deliberate emphasis on conceptual understanding versus theoretical background, educators often employ real data, active learning, and technology. The "Rice Virtual Laboratory in Statistics" (#9751307) by David Lane, Joe Austin, David Scott, Keith Baggerly, and Miguel Quinones is a webbased resource for students and teachers of statistics. The site (http://onlinestatbook.com/rvls.html) houses the Hyperstat Online textbook, case studies, simulations, and some basic analysis tools. The intended progression is for users to explore a statistical concept demonstrated in a case study, which will link them to explanatory material from the online textbook, leading them to simulations of the concept through Java applets, ending with the users’ own experiments, providing the student or teacher with a thorough lesson and deeper conceptual understanding (Lane, Austin, Scott, Baggerly, Quinones 2000). Associated with the Rice Virtual Labs is "Online Statistics Education: An Interactive Multimedia Course of Study" (#0089435) by David Lane, David Scott, Rudy Guerra, Michelle Hebl, and Daniel Osherson. This online statistics course (http://onlinestatbook.com/) contains lecture materials, simulations, selftesting, and real data from case studies, and can be modified depending on audience level. To learn the concepts, students answer a series of questions, then conduct simulations to see if they were correct, and then answer the questions again (Lane, Scott, Guerra, Hebl, Osherson 2004). Beth Klingner and Nira Herrmann make use of Lane’s online course in their project, "Enhancing the Mathematical Foundation of Students through Online Course Modules" (#0311016). They adapted the materials into modules designed to teach quantitative and analytical skills in relevant contexts (Klingner, Herrmann 2003). For those with little or no computer access, "Laboratory Lessons for DiscoveryBased Statistics" (#9650581) by Richard Scheaffer produced handson, studentdirected lessons that teach fundamental concepts like randomness, sampling distributions, confidence, and significance, which can include, but do not require, computer use (Scheaffer, 1996).
Many projects that involve active learning do so through computer laboratory modules, activities, or courselong projects. These activities are designed to help students practice the scientific process while gaining deeper conceptual understanding. Both "Fostering Conceptual Understanding Using a ‘HandsOn’ Approach in Undergraduate Statistics" (#9452320) by Danuta Bukatko and Patricia Kramer and "A Statistical Laboratory for Active Learning" (#9550891) by Richard Scheaffer created interactive, handson computer module activities that help students learn statistical concepts using graphing and analysis (Bukatko, Kramer 1994; Scheaffer 1995). "An ActivityBased Statistics Course for Engineers" (#0126815) by Steven Butt, Bob White, and Tycho Fredericks gives students the opportunity to collect their own data and solve realworld problems in weekly labs and workshops, while "Development of an Inquirybased Curriculum in Ecology" (#0088369) by Richard Tankersley and John Morris has integrated laboratory modules into a four semester sequence of ecology courses, where students sharpen their thinking skills through statistical technique exercises and selfdesigned investigations (Butt, White, Fredericks 2004; Tankersley, Morris 2005).
Another active learning project that spans courses is "Promoting Undergraduate Research through Development of Two Interdisciplinary Research Methods/Statistics Courses and Increased Support of Student Research," (#0126435) by Kathy Silgailis and Vishwa Bhat. Resulting from this project is a twosemester sequence of courses for science majors in which students complete a sequencelong project. In the first semester, students develop a research question and hypothesis while learning basic statistical procedures, and in the second semester, they refine the question, design and conduct data collection, and prepare written and oral presentations of their findings. This indepth project also includes software training and laboratory activities (Silgailis, Bhat 2005).
It would be very tempting to say that any project that implements some type of technology meets the technology recommendation; however, technology for the sake of technology is not what this guideline recommends. Utilizing technology means providing access to hardware or software that enhances the learning of statistics, especially computational or simulation software. For instance, Roxy Peck and James Daly’s "Studio Environment for Introductory Statistics" (#9750663) established a laboratory classroom so that students could complete lab activities, projects and simulations to help them learn statistics (Peck, Daily 1997).
Simulation software is useful technology for teaching statistics. Sampling SIM, software developed by Joan Garfield, Robert delMas, and Beth Chance through "Tools for Teaching and Assessing Statistical Inference" (#9752523), helps students with conceptual understanding by allowing them to make and test predictions. The simulation software provides instructional materials and is available freely over the web at http://www.tc.umn.edu/~delma001/stat_tools/ (Garfield, delMas 2000).
A larger software program focused on data analysis was developed through "A Data Analysis Exercise Server for Introductory Statistics Courses" (#9980973) by Todd Ogden and Webster West and "DoStat.com: A Web Site for Educational Data Analysis and Assessment" (#0226097) by Webster West and James Lynch. "StatCrunch," initially called "WebStat," is lowcost online computational software found at http://www.statcrunch.com. This Excel compatible software allows students to upload their own data and perform descriptive statistics, hypothesis testing, confidence intervals, regression, ANOVA, categorical or quantitative graphing, and more (West, Wu, Heydt 2004). A project utilizing StatCrunch is "Visualizing Statistics – A Online Introductory Course" (#9950671) by Alexander Kugushev and CyberGnostics, Inc. This online course offers explanatory text, applets, real data, testing, and "StatCrunch" analytical software (CyberGnostics 2005).
Determining if you are meeting your instructional goals for your students is difficult without proper assessment instruments. Two projects have been funded which provide access to statistical assessments. The "Webbased ARTIST Project" (#0206571) by Joan Garfield, Beth Chance, and Robert delMas consists of an assessment builder of over 1,000 items varying by format, level, and statistical topic as well as project ideas, article critiques, group work options, and scoring guidelines. References of works published on assessment topics are also available on the site, https://app.gen.umn.edu/artist/ (Garfield, Chance 2004). "Statistical Concepts Inventory (SCI): A Cognitive Achievement Tool in Engineering Statistics" (#0206977) by Teri Rhoads, Teri Murphy, and Robert Terry is a multiple choice exam intended to measure the ability of engineering students to apply statistical concepts to realworld situations. This assessment tool includes questions related to statistical topics important in engineering such as designing and conducting experiments and analyzing and interpreting data. It is available at http://onlinestatbook.com/rvls.html/ (Rhoads, Murphy 2005).
Many of the projects previously described meet more than one recommendation. However, few projects meet all GAISE recommendations. One such project that reaches across the discipline of statistics to touch students and teachers at all levels is "CAUSEweb: A Digital Library of Undergraduate Statistics Education" (#0333672) led by Dennis Pearl and supported by the Consortium for the Advancement of Undergraduate Statistics Education (CAUSE). This digital library, found at http://causeweb.org/, includes a resource section which provides descriptions and/or reviews for statistics education materials. Students and teachers can search for resources by material type, audience level, math level, application area, or statistical topic (Green, McDaniel, Rowell 2005).
The GAISE College Report recommendations are the result that evolved from many years of work by the statistics education community to determine the best standards for teaching and learning introductory statistics. With so many NSFfunded projects achieving the ideals described in GAISE, it is apparent that the NSF supports the implementation of these recommendations. NSFsupported resources described in this paper provide a good starting place for introductory statistics teachers to find ideas to help them implement one or more of the GAISE recommendations. The Appendix includes additional "information" on over 100 NSFsupported projects in introductorylevel statistics education. 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.
Award Number 
Title 
PI 
Start Date 
NSF Program 
Award Amount 
Institution 
9950494 
Computer
Enhanced Mathematics Instruction 
Addison Frey 
June 1, 1999 
AI 
$25,429 

9950161 
An Interactive
Learning Environment in Statistics: Integrating Multimedia Laboratory
Exercises and Courseware into the Statistics Curriculum 
Deborah A. Nolan 
July 1, 1999 
AI 
$99,238 
U. California 
9950509 
HighTech.,
ProjectBased Beginning Algebra and Statistics Course for TwoYear Colleges 
Sue E. Stokley 
August 1, 1999 
AI 
$32,847 

9950628 
Beyond Mapping
and Reporting: Improving Students' Skills in Science and Analysis for
Geography, Environmental Studies, and Ecology 
Robert Werner 
August 1, 1999 
AI 
$42,200 

9972494 
Integrated
Statistics and Computer Application Courses 
Melinda A. Holt 
August 1, 1999 
AI 
$87,577 

9950229 
Quantitative
Reasoning and Informed Citizenship: Implementing an Activitybased Laboratory
Course 
Kay Somers 
September 1, 1999 
AI 
$79,412 

9950856 
New Laboratory
and Integrated Course Materials to Improve the Psychology Curriculum 
Scott Ottaway 
September 1, 1999 
AI 
$86,276 
West. 
9980995 
Using the
LaCEPT Model to Reform an Elementary Statistics Course 
Frank Neubrander 
January 1, 2000 
AI 
$74,063 
LSU, A&M Coll. 
9952620 
Development of
Laboratory and Field Experience Based Course in Asphalt Technology for Civil
Engineering Undergraduate Students 
Rajib Mallick 
February 1, 2000 
AI 
$31,479 

9981172 
A Statistics
Undergraduate Computing Laboratory 
Robert L. Gould 
March 1, 2000 
AI 
$69,181 
UCLA 
0087680 
A
Multifunctional Technology Classroom for the Teaching of DataIntensive
Statistics 
Steven C. Patch 
January 1, 2001 
AI 
$49,450 
UNC, Asheville 
0088369 
Development of
an Integrated Inquirybased Curriculum in Ecology 
Richard Tankersley 
February 1, 2001 
AI 
$201,134 
Florida Inst. Of Tech. 
0088377 
Political
Analysis in an Experiential/ Collaborative Setting 
Allan McBride 
March 15, 2001 
AI 
$45,642 
Univ. Southern Miss. 
0088422 
Adaptation and
Implementation of Computer Technology into the Mathematical Science
Curriculum 
T. Len Miller 
July 1, 2001 
AI 
$93,011 
MSU 
0126815 
An
ActivityBased Statistics Course for
Engineers 
Steven E. Butt 
January 23, 2002 
AI 
$52,139 

0126914 
Integrating
Mathematics and Statistics into the Biology Curriculum 
Eric Marland 
March 1, 2002 
AI 
$159,583 
Appalachian S.U. 
0126682 
A Multistage,
Technologyintensive Approach to Statistics Instruction 
Jeff Knisley 
May 1, 2002 
AI 
$124,996 
ETSU 
0126435 
Promoting
Undergraduate Research through the Development of Two Interdisciplinary
Research Methods/ Statistics Courses and Increased Support of Student Research 
Kathy Silgailis 
July 1, 2002 
AI 
$197,975 
Will. 
0309751 
IBASE:
Integrating Biology and Statistics Education 
James J. Watrous 
July 1, 2003 
AI 
$89,188 

0311016 
Enhancing the
Mathematical Foundation of Students through Online Course Modules 
Beth Klingner 
August 15, 2003 
AI 
$164,985 
Pace University, NY 
0310932 
Implementing
Activitybased Cooperative Learning and Technology (ACT Curriculum) in
Statistics Courses for Nonmajors and K12 Preservice Teachers 
Carl M. Lee 
September 1, 2003 
AI 
$177,052 
Central Michigan Univ. 
0311579 
Collaborative
Research: Adapting and Evaluating Online Materials for Undergraduate
Statistics using LONCAPA Technology 
Deborah A. Kashy 
September 15, 2003 
AI 
$35,078 
Michigan State Univ. 
0311695 
Collaborative
Research: Adapting and Evaluating Online Materials for Undergraduate
Statistics using LONCAPA Technology 
Jennifer G. Boldry 
September 15, 2003 
AI 
$47,125 
Montana State Univ. 
0410115 
ServiceLearning
in Chemistry: Lead in Soil from Vehicle Emissions 
Hal Van Ryswyk 
September 1, 2004 
AI 
$41,227 

0411041 
Integrating
Data Analysis into the Curriculum: Responding to the Scientific Literacy Gap
Among Undergraduate Students in the Social Sciences 
Esther Wilder 
September 1, 2004 
AI 
$175,000 
CUNY, Herbert Lehman 
0206571 
The Webbased
ARTIST Project 
Joan Garfield 
August 15, 2002 
ASA 
$551,094 
UMN, 
0206977 
The
Statistical Concepts Inventory (SCI): A Cognitive Achievement Tool in
Engineering Statistics 
Teri R. Rhoads 
September 1, 2002 
ASA 
$499,999 

9254087 
A Modular
Laboratory and ProjectBased Statistics Curriculum 
Joseph D. Petruccelli 
January 1, 1993 
CCD 
$165,000 

9254182 
Realizing the
Power of Computers in Business Statistics Instruction: A Next Step 
Ronald Tracy 
February 1, 1993 
CCD 
$60,029 

9354506 
Developing
Statistical Understanding through Interactive Computing/Graphics 
Leo Breiman 
March 1, 1994 
CCD 
$166,637 
U. California 
9354419 
Constructing
Knowledge of Statistical Concepts through Modern Technology 
Dennis D. Wackerly 
May 1, 1994 
CCD 
$99,992 

9354592 
Change:
Current Studies of Current Chance Issues, Phase II 
J. Laurie Snell 
July 1, 1994 
CCD 
$209,914 

9455393 
New
Engineering Course with a Virtual Computer Laboratory 
Norma F. Hubele 
February 1, 1995 
CCD 
$100,600 
Arizona State Univ. 
9455300 
New Geology
Laboratories: Interactive Data Acquisition, Analysis, and Multimedia Modules
of Geologic Phenomena, Part II 
Dennis Hodge 
May 1, 1995 
CCD 
$75,000 
SUNY, Buffalo 
9455601 
Coupling
Mathematics and Life Science Courses 
Marlene Wilson 
June 1, 1995 
CCD 
$51,293 

9455578 
Revitalizing
Introductory Statistics for Engineering by Capitalizing on Interdisciplinary
Cooperation and StateoftheArt Technology 
Panickos N. Palettas 
August 1, 1995 
CCD 
$57,866 
Virginia Tech 
9696174 
Revitalizing
Introductory Statistics for Engineering by Capitalizing on Interdisciplinary
Cooperation and StateoftheArt Technology 
Panickos N. Palettas 
January 1, 1996 
CCD 
$140,975 

9555073 
Interactive
Video Resources for Learning Statistics 
William I. Notz 
March 1, 1996 
CCD 
$103,701 

9554805 
Synergistic
Learning in Biology and Statistics (SLIBS) 
Robert V. Blystone 
June 1, 1996 
CCD 
$246,336 

9555233 
Interactive
Video Resources for Learning Statistics 
Paul F. Velleman 
June 1, 1996 
CCD 
$51,598 

9653153 
Earth Math
Phase 3; Calculus and Statistics for a New World 
Nancy Zumoff 
January 1, 1997 
CCD 
$22,800 
Kennesaw S.U. 
9653267 
Revitalizing
the Study of Probability through Applications, Technology, and Collaborative
Learning 
Michael Bean 
September 1, 1997 
CCD 
$180,001 

9752428 
A
Probability/Activity Approach for Teaching Introductory Statistics 
James Albert 
January 1, 1998 
CCD 
$50,000 

9752559 
Probability
and Surprise: Animations and Simulations 
Susan P. Holmes 
January 1, 1998 
CCD 
$99,970 

9752523 
Tools for
Teaching and Assessing Statistical Inference 
Joan B. Garfield 
February 1, 1998 
CCD 
$100,021 
UMN, 
9752645 
Intersection
of Population Biology and Mathematics 
Jane Gallagher 
June 1, 1998 
CCD 
$150,000 
CUNY 
9850035 
Science
Education for Tomorrow 
Elizabeth Boylan 
September 1, 1998 
CCD 
$196,152 

9996235 
Probability and
Surprise: Animations and Simulations 
Susan P. Holmes 
January 1, 1999 
CCD 
$62,048 

9653224 
Revitalizing
Classroom Teaching and Learning: A Beginning for 
Elizabeth Higgins 
February 1, 1997 
CCD/ATE 
$99,799 

9752185 
Integrating
Pedagogical and Curriculum Theory with Teaching Practice Throughout all
Mathematics and Science Courses in the College of Arts & Sciences and
Evaluating ... 
Edward Dubinsky 
March 1, 1998 
CCD/CETP 
$100,000 
Georgia State Univ. 
9354529 
Informed
Statistical Reasoning in an Uncertain World: Situated Simulations for
Undergraduates 
Sharon Derry 
June 1, 1994 
CETP 
$202,316 
UWMadison 
9950671 
Visualizing
Statistics  An OnLine Introductory Course 
Alexander Kugushev 
October 1, 1999 
EMD 
$260,484 
CyberGnostics Inc. 
9980796 
Development of
an Interactive Tutorial on Statistical Design and Analysis of Experiments 
John O'Haver 
February 1, 2000 
EMD 
$79,898 

9980973 
A Data
Analysis Exercise Server for Introductory Statistics Courses 
R. Todd Ogden 
May 1, 2000 
EMD 
$75,000 
USC, Columbia 
0088703 
Development of
Sports Statistics Modules for Introductory Statistics Classes 
James H. Albert 
January 1, 2001 
EMD 
$67,258 

0089435 
Online Statistics
Education: An Interactive Multimedia Course of Study 

February 1, 2001 
EMD 
$401,990 
Will. Marsh Rice Univ. 
0126855 
CaseBased
Reasoning for Engineering Statistics 
George C. Runger 
December 1, 2001 
EMD 
$74,622 
Arizona State Univ. 
0126433 
Teaching
Psychological Research Methods with Online Examples 
William Maki 
April 15, 2002 
EMD 
$102,147 

0226097 
DoStat.com: A
Web Site for Educational Data Analysis and Assessment 
R. Webster West 
June 15, 2002 
EMD 
$130,002 
USC, Columbia 
0230803 
Stem and
Tendril: Vertically Integrated Statistics Laboratories 
Andrew Poje 
January 15, 2003 
EMD 
$74,836 
CUNY Staten Island 
0341210 
Improving the
Quality of and Access to Undergraduate Statistics Education 
Fred Speed 
January 1, 2004 
EMD 
$74,826 
Texas A&M Univ. 
0341529 
An
AudioTactile Curriculum to Support Visually Impaired Statistics Students 
Karen Gourgey 
February 15, 2004 
EMD 
$30,032 
CUNY, Baruch 
9752705 
UFE: Teaching
ComputerIntensive Resampling Techniques 
Amer. Stat. Assoc. 
February 15, 1998 
EMD/UFE 
$60,000 
Amer. Stat. Assoc. 
9250330 
Behavioral
Sciences Computer Laboratory 
James Raymondo 
July 1, 1992 
ILI 
$25,280 

9351126 
Data Analysis
Laboratory 
Loren Haskins 
April 1, 1993 
ILI 
$32,355 

9351926 
Elementary Statistics
Computer Laboratory 
Louis M. Friedler 
April 1, 1993 
ILI 
$19,259 

9352131 
An Interdisciplinary
Laboratory for Data Acquisition, Analysis, and Modeling 
Dwight Krehbiel 
April 1, 1993 
ILI 
$49,560 

9351035 
Discovering
Statistics: A Laboratory Approach 
Richard L. Scheaffer 
June 1, 1993 
ILI 
$25,000 

9351493 
Computational
Classroom Facility for Biometry Courses 
Charles McCulloch 
June 1, 1993 
ILI 
$40,000 

9352076 
Technology
for: Improvements of Mathematical Concepts and Initiation to Professional
Tools 
Karla Foss 
June 1, 1993 
ILI 
$35,000 
Pellissippi STCC 
9352110 
Instrumentation
for Novel Laboratory Instruction in Undergraduate Statistics Curricula 
Walter R. Pirie 
June 1, 1993 
ILI 
$52,646 
Virginia Tech 
9350746 
A Computer Lab
for Biological Statistics 
Daniel E. Wujek 
July 1, 1993 
ILI 
$26,887 
Central Michigan Univ. 
9352312 
Novel
Laboratory Instruction in Undergraduate Statistics Curricula 
Panickos N. Palettas 
August 1, 1993 
ILI 
$90,221 
Virginia Tech 
9451814 
Multidisciplinary
Statistics Curriculum and Computing Laboratory 
Chris Noble 
June 1, 1994 
ILI 
$38,560 

9452229 
Interactive
Computerized Statistics Classroom 
Louise Hainline 
June 1, 1994 
ILI 
$70,072 
CUNY, Brooklyn 
9451972 
InClass
Experimental Learning in Four Fundamental Courses 
John Stone 
July 1, 1994 
ILI 
$70,000 

9452622 
Developing a
Computer Lab for the Technology Enhanced Teaching of Undergraduate Statistics 
Judith Treas 
August 1, 1994 
ILI 
$55,000 
U. California, 
9451398 
A Computer
Classroom for Introductory Statistics 
Joseph D. Petruccelli 
August 15, 1994 
ILI 
$53,348 

9452156 
Enhancement of
Statistics, Research Methods and Experimental Psychology Laboratories 
Virginia A. Diehl 
September 1, 1994 
ILI 
$33,659 
West. 
9452320 
Fostering
Conceptual Understanding Using a "HandsOn" Approach 
Danuta Bukatko 
September 1, 1994 
ILI 
$17,640 

9550891 
A Statistical
Laboratory for Active Learning 
Richard L. Scheaffer 
May 1, 1995 
ILI 
$14,940 

9551850 
Computer
Classroom for Statistical Instruction 
Ronald L. Tracy 
August 1, 1995 
ILI 
$65,000 

9551275 
Computer Stat
Lab 
Patricia R. Wilkinson 
September 1, 1995 
ILI 
$29,391 
CUNY, BMCC 
9551460 
From
Descriptive to Adaptive Understanding: Using Interactive Computer Simulation
in Quantitative Biology and Statistics Labs 
David G. Huffman 
September 1, 1995 
ILI 
$62,320 
Southwest Texas S.U. 
9552311 
Fostering
Creativity, Teamwork, and Scientific Thinking in Introductory Statistics
through ComputerBased Laboratories 
Peter G. Jessup 
September 1, 1995 
ILI 
$24,333 

9650048 
Interactive
Undergraduate Statistical Computing Laboratory 
John I. Marden 
May 1, 1996 
ILI 
$38,070 
UI, UrbanaChampaign 
9650871 
Computer
Laboratories in Calculus & Statistics 
Bruce Torrence 
June 1, 1996 
ILI 
$41,597 
RandolphMacon Coll. 
9696158 
Novel
Laboratory Instruction in Undergraduate Statistics Curricula 
Panickos N. Palettas 
June 1, 1996 
ILI 
$47,651 

9650645 
Mathematics
and Statistics Computer Classroom 
ILok Chang 
July 1, 1996 
ILI 
$23,680 

9651186 
Mathematics
Multimedia Presentation Classroom 
Kevin McDonald 
July 1, 1996 
ILI 
$60,000 
Mt. San Antonio Coll. 
9650032 
A
Microcomputer Laboratory for Experimental Psychology 
Sarah Ransdell 
August 1, 1996 
ILI 
$22,000 
Florida Atlantic Univ. 
9650581 
Laboratory
Lessons for DiscoveryBased Statistics 
Richard L. Scheaffer 
August 1, 1996 
ILI 
$46,620 

9650659 
Department of 
Byron David 
August 1, 1996 
ILI 
$19,900 
CUNY, 
9651271 
Computer
Assisted Interdisciplinary Problem Solving in Mathematics and Science 
Samantha Prashanta 
September 1, 1996 
ILI 
$31,830 
Finger Lakes CC 
9750663 
Studio
Environment for Introductory Statistics 
Roxy Peck 
June 1, 1997 
ILI 
$61,429 
Cal Poly State Univ. 
9751571 
DataDriven
Statistics Courses in an Interactive Teaching Computer Laboratory 
Andre M. Lubecke 
June 1, 1997 
ILI 
$54,969 

9751307 
The Rice
Virtual Lab in Statistics 

July 1, 1997 
ILI 
$200,000 
Will. Marsh Rice Univ. 
9851421 
STATLAB  An
Interactive Classroom and Laboratory for Introductory Statistics 
David C. Carothers 
June 1, 1998 
ILI 
$59,936 
James Madison Univ. 
9851146 
Equipping the
Statistical Toolkit: An IntranetBased Approach to Introductory Statistics 
Gavin M. Cross 
July 1, 1998 
ILI 
$40,953 

9851321 
Computers for
an Introductory Interdisciplinary Data Analysis Course 
Laura P. Eisen 
July 1, 1998 
ILI 
$16,780 

9851559 
ComputingEnhanced
Experiential Learning in the Introductory Statistics Course 
Ann R. Cannon 
July 1, 1998 
ILI 
$21,090 

0089005 
MAA
Comprehensive Professional Development Program For Mathematics Faculty 
J Michael Person 
April 1, 2001 
ND 
$966,291 
MAA 
0088715 
Collaborative
Project on Integrating Census Data Analysis into the Curriculum 
William H. Frey 
May 15, 2001 
ND 
$522,205 

0089006 
Collaborative
Project on Integrating Census Data Analysis into the Curriculum 
Felice J. Levine 
May 15, 2001 
ND 
$417,241 
Amer. Soc. Assoc. 
0341481 
PRofessional
Enhancement Program (PREP) 
J Michael Pearson 
February 1, 2004 
ND/NSDL 
$462,690 
MAA 
0333672 
CAUSEweb: A
Digital Library of Undergraduate Statistics Education 
Dennis Pearl 
October 1, 2003 
NSDL 
$824,945 

9255447 
Statistical
Thinking and Teaching Statistics 
George W. Cobb 
March 1, 1993 
UFE 
$450,068 
MAA 
9554621 
STATS:
Statistical Thinking with Active Teaching Strategies 
Allan J. Rossman 
January 1, 1996 
UFE 
$202,844 
MAA 
9653416 
Chance
Workshop 
J. Laurie Snell 
January 1, 1997 
UFE 
$87,660 

9653442 
Elementary
Statistics Laboratory Workshop 
John D. Spurrier 
March 1, 1997 
UFE 
$67,845 
USC, Columbia 
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Klingner, B. and Herrmann, N. (2003), "Enhancing the Mathematical Foundation of Students through Online Course Modules Abstract #0311016." Retrieved March 2, 2005 from NSF Online Database http://www.nsf.gov/awardsearch/.
Lane, D.M., Austin, J.D., Scott, D.W., Baggerly, K.A., Quinones, M.A. (2000), "Rice Virtual Laboratory in Statistics #9751307." DUE PIRS Search Engine. Retrieved March 2, 2005 from https://www.ehr.nsf.gov/pirs_prs_web/search/RetrieveRecord.asp?Awd_Id=9751307.
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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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