Our charge

Students entering college broadly show poor preparedness
in quantitative knowledge, skills, and reasoning (Grawe, 2012; Nilson,
2010). On our campus, typically around half of incoming freshmen are deemed
"not proficient in mathematics".
We began the development of a Quantitative
Reasoning Faculty Learning Community (QRFLC) in Fall, 2013. This Faculty
Learning Community was designed to explore broad issues related to the
development of campuswide approaches to assessing student quantitative
reasoning capabilities, and, more importantly, to developing curricular
approaches aimed at helping students improve their quantitative reasoning
capabilities throughout their undergraduate experiences.
By combining attention to definitions, curriculum
development and assessment into one comprehensive learning community project,
and engaging in this work alongside other campus projects aimed at reforming
General Education and developing baccalaureate learning outcomes, we believe
we have an opportunity to significantly transform our academic program
as it relates to quantitative reasoning. 

Critical Areas

Move the cursor over an icon to see the definition of each
of these critical areas 

Approximation / estimation 

Mathematical models 

Tables and graphs 

Algebra 

Geometry 

Statistics 



Pedagogical Materials

Rain  Quantitative Reasoning in a Literature
Class (Link) 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 onesidedness? 
Estimation (Link)
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 (Link)
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 (Link)
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
(Link) Students analyze an excerpt from an article in a local newspaper,
and judge its veracity. 
Understanding functions (Link)
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 (Link)
How Cuba Survived Peak Oil â€“ 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. 

Definition

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


Note: links open in a new tab 
Presentations

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


About us
The Quantitative Literacy Group at Stan State is open to all faculty members,
staff and students. The current members include:

Dr. Tom Carter, Computer Science

Dr. Koni Stone, Chemistry

Dr. JungHa An, Mathematics

Dr. Melanie Martin, Computer Science and Mathematics

Dr. Sandra Garcia Sanborn, Philosophy and Modern Languages

Dr. Ian Littlewood, Physics

Dr. Chris Nagel, Philosophy and Modern Languages

Dr. Augustine Avwunudiogba, Geography


