Quantitative Literacy Electronic Resources

Neil Lutsky , Professor of Psychology at Carleton College and Director of the Quantitative Inquiry, Reasoning, and Knowledge (QUIRK) initiative at discusses his initiation into quantitative literacy (Flash 3.1 MB).

Dorothy Wallace, Professor of Mathematics and Principal Investigator of the Mathematics Across the Curriculum project at Dartmouth College points out the core values of quantitative literacy (Flash 4.9 MB)..

Deann Leoni, Instructor of Mathematics at Edmonds Community College and Co-Principal Investigator of Mathematics Across the Community College Curriculum project discusses unanticipated outcomes (Flash 4.9 MB).

Rebecca Hartzler, Instructor/Coordinator of Physics and Engineering at Seattle Central Community College and Co-Principal Investigator of the Mathematics Across the Community College Curriculum Project encourages community colleges to take a leadership role in quantitative literacy (Flash 4 MB)

What is Quantitative Literacy?

 

Simply put one could say that QL is “the ability to understand and use numbers and data in everyday life.” [1] Deconstructing the term QL – to focus upon ‘literacy’ - Robert Orrill in the preface to Mathematics and Democracy identifies QL as a “cultural field where language and quantitative constructs merge and are no longer one or the other.” [2]

Additionally, Orrill calls upon the work of Lawrence Cremin who distinguishes between inert literacy  a level of verbal and numerate skills required to comprehend instructions, perform routine procedures and complete tasks in a routine manner,”

and liberating literacy “command of both the enabling skills needed to search out information (in our case – quantitative information) and the power of mind necessary to critique it, reflect upon it, and apply it in making decisions.” [3] This level of translation between skills our students learn in their math courses and their disguises in other disciplines (often assumed but seldom taught) requires a deep integration and long-term collaboration across the disciplines.

QL is Interdisciplinary

“Math in context” is another shorthand definition of QL.  Yet taking our direction from the definitions above we realize that a single liberal arts mathematics course will not suffice.  Indeed the inherent interdisciplinary nature coupled with the requirement of meaningful quantitative contexts demands that we as mathematics

educators forge respectful relationships with our colleagues across the disciplines.

Combined these definitions and others presented along the side bar of this page bring us to an understanding the complexity of the call to coordinate across the disciplines to create a curriculum that effectively supports quantitative literacy in our colleges.

Two Year Colleges Are Ready to Take the Lead in QL

Finally the small school infrastructure of most two-year colleges tailors well to the need for intense interdisciplinary collaboration in formulating, implementing and assessing effective QL curricula.  We at two-year colleges are more likely to know our colleagues in other disciplines, have successful experiences in implementing innovative curriculum, and are rewarded for teaching innovations.  We are well

situated to make the institutional and degree changes that will assure a deep and

lasting implementation of quantitative literacy curriculum that will assure that our students “possess the power and habit of mind to search out quantitative information, critique it, reflect upon it, and apply it in their public, personal and professional lives.”

1. Madison, Bernard L and Lynn Steen, eds. Quantitative Literacy:  Why Numeracy Matters for Schools and Colleges. Princeton, NJ. NCED, 2003.
2. Steen, Lynn Arthur, ed. Mathematics and Democracy. The Case for Quantitative Literacy. Princeton, NJ: NCED, 2001.
3. Cremins, Lawrence A.  American Education:  The Metropolitan Experience 1876-1980. New York:  Harper & Row, 1988 (as quoted by Robert Orill in Mathematics and Democracy.)
   
   

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This material is based upon work supported by the National Science Foundation under Grant No. 0410842. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.