Quantitative Reasoning


Facilitator Members
Thomas Carter, Computer Science - Koni Stone, Chemistry
- JungHa An, Mathematics
- Melanie Martin, Computer Science and Mathematics
- Sandra Garcia Sanborn, Philosophy and Modern Languages
- Ian Littlewood, Physics
- Chris Nagel, Philosophy and Modern Languages
- Augustine Avwunudiogba, Geography

Defining Quantitative Literacy 

Quantitative Literacy (QL) (also known as Quantitative Reasoning (QR) is a "quantitative habit of mind", proficiency, and comfort in dealing with and rationally processing numerical data. Individuals with strong QL skills possess the ability to analyze quantitative problems in everyday life situations using logical reasoning steps. They are able to read and understand numerical data. They can create valid arguments based on quantitative evidence and know how to interpret their conclusions. They are capable of clearly communicating their analyses and arguments in a variety of formats (including words, tables, graphs, mathematical equations and models, as appropriate).

Expanded Definition 

The formal definition of Quantitative Literacy implies competency in different fields of basic mathematics, and their application to diverse problems in the sciences, business and administration, politics, economics, and in everyday life. Most importantly, QL requires an understanding of the mathematics that is deeper than mere memorization of, and facility with, calculation procedures. Possession of strong QL skills requires competency in critical areas:

  • Approximation / estimation
  • Mathematical models
  • Tables and graphs
  • Algebra
  • Geometry
  • Statistics

Critical Areas

calculator Approximation / estimation
flow chart Mathematical models
graph  Tables and graphs
square root Algebra
geometry tools Geometry
statistics Statistics


Rubrics have been developed for the overall goal of Quantitative Literacy and for each of the critical areas.

Overall Quantitative Literacy

Critical Areas

Pedagogical Materials 

Rain - Quantitative Reasoning in a Literature Class
Is it viable to have students engage in quantitative reasoning in a foreign language literature class?  Besides the mathematical practice gained, can this activity also open the door for students to consider that bridging disciplines can broaden the possibilities offered by a text while helping us avoid falling into cultural one-sidedness?

This exercise was meant to develop a class estimate of the amount of radioactive material which was given to Alexander Litvinenko, a Russian dissident living in exile in London. All the required physics has been covered already in class, after which there are two remaining problems

Sources of Energy
From an accompanying diagram which shows the different means by which electricity in the US is generated, students are asked to evaluate the consequences of eliminating either nuclear or coal power stations from the nation's energy generation. Given as an in class assignment, August 24 2017

Double Radioactive Decay
From a diagram containing two decay curves corresponding to two different isotopes students determine relative populations at different times. Assignment does not require algebra, but instead uses graphical representation of data

Astronauts and Solar Radiation
Students analyze an excerpt from an article in a local newspaper, and judge its veracity.

Understanding Functions
Some examples of questions where students have to answer questions based on the algebraic relationships between variables without being able to substitute numbers

The Power of Community
How Cuba Survived Peak Oil – By tracking their food consumption students were asked to estimate the gallons of water needed to produce the food they consumed in a typical day.

Students are give 6 plots of the population of 5 different isotopes and have to assign each isotope to one of the plots. The assignment is not numerical, but relies on the ability to understand graphical representation of the physical situation.


"Quantitative Reasoning and / or Quantitative Literacy", CSU Stanislaus Quantitative Reasoning Working Group San Jose Meeting, April 22, 2016 (Longer version)


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