Quantitative Literacy Resources

Bernard Madison

Note that the resources on this page do not include the seminal works of Lynn Steen found at Steen. They do include the works Lynn Steen co-edited with Bernard Madison (shown above). Bernard Madison was the founder and first President of the National Numeracy Network.

The need for Quantitative Literacy (QL) is generally accepted. But the content and teaching of QL is under active discussion and debate.  The quotes from the following publications present some of that discussion.

All of the books shown here involve papers compiled by the editors: Lynn Steen, Bernard Madison and Richard Gillman. In the Steen-Madison books, Robert Orrill (to name just one) worked tirelessly in support of QL as have the members of the Mathematics and Democracy Design team and the members of the National Numeracy Steering Committee. [Note: this US National Numeracy effort is different from the National Numeracy Strategy in the UK.].

[While the following are all quotes from the listed publications, this page has not been reviewed or approved by any of those mentioned on this page.]

Calculation vs. Context: Quantitative Literacy and Its Implications for Teacher Education  (2008)
Editors: Bernard L. Madison and Lynn Arthur Steen

Calculation vs Context “Innovative and more effective quantitative literacy education is urgent both as a part of revitalization of liberal education and as a response to the increasing quantitative reasoning demands of US society. In the information age of today and tomorrow, lives are increasingly governed by numbers. The ubiquity of data and analyses of data require one to use sound quantitative reasoning to cope intelligently with the requirements of citizenship, job and family, and to be prepared for a healthy, happy and productive life. But quantitative reasoning for the practical circumstances of life is not part of the school or college curriculum — even for those taking a large complement of mathematics and science courses. In particular, it is not part of the college education of prospective teachers, and it is not a major part of the current national agenda reshaping school curricula. Achieving a quantitatively literate citizenry requires serious changes in the education of teachers and the curricula they teach. Thus, the need for a conference on Quantitative Literacy and Its Implications for Teacher Education.” — From the letter of invitation to conference participants

“Innovative and more effective quantitative literacy education is urgent both as a part of revitalization of liberal education and as a response to the increasing quantitative reasoning demands of US society. Thus, the need for a conference on Quantitative Literacy and Its Implications for Teacher Education.”  MAA Bookstore.

“This volume contains the broadest interpretation yet of quantitative literacy (QL) as it should play out across the school and college curriculum. Nine commissioned essays on QL and teacher education by scholars in eight academic disciplines both challenge and expand more traditional views of QL. These essays, introductions by editors Bernard Madison and Lynn Steen, and brief summaries of discussions summarize the proceedings of a June 2007 multi-disciplinary conference held at Wingspread Conference Center and sponsored by the MAA's NSF-funded PMET project.”  MAA 2008 Fall/Winter Catalog

Conference Steering Committee Stanley Katz, Princeton University Bernard L. Madison, University of Arkansas Robert Orrill, National Council on Education and the Disciplines Richard Scheaffer, University of Florida Carol Geary Schneider, Association of American Colleges and Universities Lynn Arthur Steen, St. Olaf College Corrine Taylor, Wellesley College Alan Tucker, State University of New York at Stony Brook

Order from the MAA Bookstore.  See under Quantitative Literacy or search on title. Not available under author.
PDF available from QL page.

Table of Contents

INTRODUCTION:

Keynote Presentation:

Commissioned Papers:

Current Practices in Quantitative Literacy   (2006)
Editor: Richard Gillman

Current Practices in Q/LCurrent Practices in Quantitative Literacy present a wide sampling of efforts being made on campuses across the country to achieve our common goal of having a quantitatively literate citizenry. Colleges and universities have grappled with complicated issues in order to define quantitative literacy within their own communities and to implement appropriate curriculum. It is clear that any quantitative literacy program must be responsive to the local conditions of an institution including its mission, its student clientele, its history and its resources.

Although the programs and courses described in this volume only represent a sample of what is happening in the community, some trends do seem to be apparent. There is consensus that the mathematical skills necessary to be quantitatively literate include elementary logic, the basic mathematics of financial interest, descriptive statistics, finite probability, an elementary understanding of change, the ability to model problems with linear and exponential models, estimations and approximation, and general problem solving. It is clear that many of our students enter college with minimal mastery of these skills and their application.

The essays suggest that we have moved forward a long way in our understanding of quantitative literacy and our ability to implement effective programs to teach it. Read the stories of other institutions who have worked through some of these issues and begin a dialogue on your own campus.”  Source: MAA Bookstore Review

Order from the MAA Bookstore. (Search on Gillman)

Table of Contents

Introduction: Rick Gillman  “The essays do indicate that there is a consensus on the mathematical skills necessary to be quantitatively literate. These include elementary logic, the basic mathematics of financial interest, descriptive statistics, finite probability, an elementary understanding of rates of change, the ability to model problems with linear and exponential models, estimation and approximation, and general problem solving. In addition, the essays suggest that many of our students enter college with minimal mastery of these skills and their applications.”

History and Context

  • Some Historical Notes: Linda Sons
  • Issues, Policies, and Activities in the Movement for Quantitative Literacy: Susan L. Ganter
  • What Mathematics Should All College Students Know?: William L. Briggs

Interdisciplinary and Interdepartmental Programs

  • Quantitative Methods for Public Policy: David Bressoud
  • The Quantitative Requirement at Juniata College: John F. Bukowski
  • Quantitative Literacy at Dominican University: Paul R. Coe and Sarah N. Ziesler
  • The Quantitative Reasoning Program at Hollins University: Caren Diefenderfer, Ruth Doan and Christina Salowey
  • Decade of Quantitative Reasoning at Kalamazoo College: John B. Fink and Eric D. Nordmoe
  • Interconnected Quantitative Learning at Farmingdale State: Sheldon Gordon and Jack Winn
  • Quantitative Reasoning Across the Curriculum: Beth Haines and Joy Jordan
  • Mathematics Across the Curriculum: Rebecca Hartzler and Deann Leoni
  • Math Across the Curriculum at UNR: Jerry Johnson
  • The Quantitative Literacy Program at Hamilton College: Robert Kantrowitz and Mary B. O’Neill
  • Quantitative Reasoning at the University of Massachusetts Boston: Maura Mast and Mark Pawlak

Quantitative Literacy Courses

  • Contribution of a First Year Mathematics Course to Quantitative Literacy: Aimee Ellington and William Haver
  • Increasing the Relevance to and Engagement of Students in a Quantitative Literacy Course: Sarah J. Greenwald and Holly Hirst
  • Quantitative Reasoning: An Interdisciplinary, Technology Infused Approach: David Jabon
  • General Education Mathematics: A Problem Solving Approach: Jesús Jimenez and Maria Zack
  • Quantitative Reasoning and Informed Citizenship: A Relevant Hands-on Course: Alicia Sevilla and Kay Somers
  • A QL Program at a Large Public University: Linda Sons
  • Quantitative Reasoning at Wellesley College: Corrine Taylor

Advising, Assessment, and Other Issues

  • Designing a QL Program to Match Student Needs and Interests: AbdelNaser Al-Hasan
  • Quantitative Literacy as an Integral Component of Mathematics Curriculum, Case at North Dakota State University: Doğan Çömez & William O. Martin
  • Case Study of Assessment Practices in Quantitative Literacy: Rick Gillman
  • The Quantitative Literacy Requirement at Alma College: Frances B. Lichtman
  • Traveling the Road Toward Quantitative Literacy: Richard J. Maher
  • Quantitative Literacy Course Selection: Carrie Muir
  • About the Editor

Achieving Quantitative Literacy  (2004)  MAA or Amazon
An Urgent Challenge for Higher Education (edited by Lynn Arthur Steen).  See Steen.

Quantitative Literacy
Why Numeracy Matters for Schools and Colleges (2003) MAA (downloadable) or Amazon
Edited by Bernard L. Madison and Lynn Arthur Steen

Quantitative Literacy “Quantitative literacy, in my view, means knowing how to reason and how to think and it is all but absent from our curricula today.” Users of quantitative information “have to learn how to think for themselves, and that is what an education in quantitative reasoning can teach them.”  Gina Kolata (1997)

“The attention to quantitative reasoning that she [Gina Kolata, see above] thinks so essential to sound judgment simply does not exist in the academic programs of most of our schools and colleges.  Robert Orrill

“To expand the conversation about QL, the NCED subsequently sponsored a national forum, Quantitative Literacy: Why Numeracy Matters for Schools and Colleges, held at the National Academy of Sciences in Washington D.C. on December 1–2, 2001. This volume represents the proceedings of this Forum and includes papers commissioned as background for that Forum, essays presented at that Forum, and selected reactions to that Forum.”  Bernard Madison

  • “Numeracy lies at the intersection of statistics, mathematics and democracy. Like statistics, numeracy is centered on interpretation of data; like mathematics, numeracy builds on arithmetic and logic. But the unique niche filled by numeracy is to support citizens in making decisions informed by evidence.” “Numeracy is largely an approach to thinking about issues that employs and enhances both statistics (the science of data) and mathematics (the science of patterns). Yet unlike statistics, which is primarily about uncertainty, numeracy is often about the logic of certainty. And unlike mathematics, which is primarily about the Platonic realm of abstract structures, numeracy often is anchored in data derived from and attached to the empirical world.”  Lynn Steen, p. 62–63.
  • “Quantitative Literacy (QL), the ability to use numbers and data analysis in everyday life, is everybody's orphan. Despite every person's need for QL, in the discipline-dominated K-16 education system in the United States, there is neither an academic home nor an administrative promoter for this critical competency.”  p. 153  Bernard Madison
  • “In reality, full-bore data analysis is more than most people need to deal with the statistical issues of everyday life and work.” p. 146  “Many statisticians would probably disagree with the statement in Mathematics and Democracy that QL is ‘not the same as statistics.’ Indeed many think that a very large part of QL is statistics...”  p.147  “Those experienced with teaching statistics suggest that one way to garner administrative support [for QL across the curriculum] and foster institutional change is to tie much of QL to the statistics curriculum, everywhere it is housed.”  p.149  “Statistics and quantitative literacy have much in common. Although few would disagree with this, statisticians would probably argue that QL is mainly statistics while mathematicians and mathematics educators tend to argue that QL is only partly statistics.”  p. 151  Richard Scheaffer
  • “Statistical methods are about logic as well as numbers. For this reason, as well as on account of their pervasiveness in modern life, statistics cannot be the business of statisticians alone, but should enter into the schooling of every educated person. To achieve this would be a worthy goal for statistics in the coming decades.”  Porter, 2001
  • “many aspects of statistical thinking are not about numbers as much as about concepts and habits of mind. For example, the idea of a lurking variable upsetting an apparent bivariate relationship with observational data is a conceptual idea, part of statistical thinking, but not particularly about numbers.”  p. 150  Richard Scheaffer.

Table of Contents:

Background Papers:

Forum Papers:

  • Need for Work and Learning:Addressing Societal and Workforce Needs, David Brakke; Making Mathematics Meaningful, Arnold Packer; Grounding Mathematics in Quantitative Literacy, Johnny Lott; Quantitative Literacy: A Science Literacy Perspective, George Nelson; Learning and Work in Context, William Steenken; Of the Teachers, by the Teachers and for the Teachers, Roger Howe; Impediments to and Potentials for Quantitative Literacy, J. T. Sutcliffe.
  • Policy Perspectives:Say What you Mean (and Mean What You Say), Janis Somerville; Education Policy and Decision Making, Margaret Cozzens; Policies on Placement and Proficiency Tests: A Community College's Role, Sadie Bragg; Standards are Not Enough: Challenges of Urban Education, Judith Rizzo; Creating Networks as a Vehicle for Change, Susan Ganter.
  • International Perspectives:Numeracy in an International Context, Lynn Steen; Quantitative Literacy and Mathematical Competencies, Morgan Niss; Defining Mathematical Literacy in France, Michel Merle; What Mathematics for All?, A. Geoffrey Howson; Numeracy: A Challenge for Adult Education, Mieke van Groenestijn; The Role of Mathematics in Building a Democratic Society, Ubiratan D'Ambrosio.
  • Reflection Papers:Why Are We Here?, Jeanne Narum; Quantitative Literacy Goals: Are We Making Progress?, Rita Colwell; What Have We Learned... and Have Yet to Learn?, Hyman Bass; Reflections from several forum participants.

Mathematics and Democracy
The Case for Quantitative Literacy (2001) Edited by Lynn Steen  MAA or PDF.  See Steen.

Why Numbers Count
Quantitative Literacy for Tomorrow's America (1997)  Edited by Lynn Steen  College Board.  See Steen.

1994: MAA Quantitative Reasoning for College Graduates

Quantitative Reasoning for College Graduates: A Complement to the Standards. Committee on the Undergraduate Programs in Mathematics (CUPM) Subcommittee on Quantitative Literacy Requirements. Washington DC: Mathematical Association of America.
Linda R. Sons, Northern Illinois University (Chair).

The report (in its Part 2) presented this over-arching goal:

  • The foremost objective of both liberal and professional types of higher education should be to produce well-educated, enlightened citizens, who can reason cogently, communicate clearly, solve problems, and lead satisfying, productive lives.

The report (in its Part 2) presented this goal for quantitative literacy:

  • Any effective attack on the problem of quantitative literacy must recognize that not all mathematical roads are narrow, algebraic ones that lead to calculus. Today's routes must offer glimpses of a broad mathematical landscape with applications prominent in the foreground. To achieve some depth along the way, college students must be taught to view landmarks from a variety of perspectives—numerical, visual, verbal and symbolic. They must learn that understanding, explanation and prediction are the real mathematical destinations, not the answers in the backs of textbooks. Unless we repeatedly immerse students in interesting quantitative settings that require drawing inferences from data, interpreting models, estimating results, assessing risks, suggesting alternatives, and even making reasonable, testable guesses, students will never see the forest for the trees.
    • In short, every college graduate should be able to apply simple mathematical methods to the solution of real-world problems.

The report in its Part 2 asserted that: “A quantitatively literate college graduate should be able to

  • interpret mathematical models such as formulas, graphs, tables, and schematics, and draw inferences from them;
  • represent mathematical information symbolically, visually, numerically and verbally;
  • use arithmetical, algebraic, geometric and statistical methods to solve problems;
  • estimate and check answers to mathematical problems in order to determine reasonableness, identify alternatives, and select optimal results;
  • recognize that mathematical and statistical methods have limits.”