ORIGINAL
REVISED
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SALEM STATE UNIVERSITY
TRACKING #
version 1516.1 - effective 9/2015-8/2016
REQUEST FOR CERTIFYING COURSE FOR GENERAL EDUCATION CATEGORY
QUANTITATIVE REASONING (QR)
Note: this form can be used ONLY for certifying a course in the indicated general education category.
The course description can be changed via this form only if the change is directly related to certifying the course in the indicated general
education category.
All other aspects of a course change must be dealt with via a CHANGE IN COURSE form.
Quantitative Reasoning Category Description: Students will use both quantitative data and abstract quantitative models to compute
useful quantities, make predictions, and draw conclusions. Students will learn to communicate using quantitative data, build or select
appropriate models, and find appropriate applications for such models.
Satisfaction of the Basic Math Competency requirement is the minimum required prerequisite for any experience that satisfies this
category requirement.
A COMPLETED QUANTITATIVE REASONING COURSE INFORMATION DOCUMENT (CID) IS INCLUDED ( NOTE THAT A COURSE
INFORMATION DOCUMENT IS NOT THE SAME AS A COURSE SYLLABUS.) THE CID MUST EXPLICITLY DOCUMENT HOW EACH OF THE
FOLLOWING CRITERIA ARE ADDRESSED.
Details and supplementary information regarding this General
Education Category and its criteria can be found at
http://www.salemstate.edu/26049.php. Note that ALL criteria must be
QR: QUANTITATIVE REASONING
addressed.
CRITERION 1: LEARNING GOALS IN THE REALM OF EMPIRICAL ANALYSIS:
1A: COMMUNICATE DATA EFFECTIVELY
1B: COMPUTE USEFUL QUANTITIES FROM DATA
1C: DRAW CONCLUSIONS USING DATA
1D: BUILD OR SELECT APPROPRIATE MATHEMATICAL MODELS (THIS INCLUDES UNDERSTANDING AND COMMUNICATING THE
ASSUMPTIONS INHERENT IN THOSE MODELS)
CRITERIA FOR GENERAL EDUCATION CATEGORY
CRITERION 2: LEARNING GOALS IN THE REALM OF ABSTRACT MATHEMATICAL MODELS:
2A: COMMUNICATE SUCH MODELS CLEARLY, INCLUDING EXPLAINING WHAT THEIR COMPONENT PIECES ARE
2B: USE SUCH MODELS TO MAKE PREDICTIONS
2C: USE SUCH MODELS TO DRAW CONCLUSIONS
2D: USE SUCH MODELS TO COMPUTE USEFUL QUANTITIES
2E: FIND APPROPRIATE APPLICATIONS FOR SUCH MODELS
DEPARTMENT:
SEMESTER/YEAR IN WHICH CERTIFICATION WILL TAKE EFFECT:
select semester select year
COURSE PREFIX AND NUMBER:
OTHER CERTIFICATIONS FOR THIS COURSE:
FULL TITLE:
CREDITS:
cr.
(FYS|QR|OC|PGR|CEA|WC|HP|CS|SR|W-I|W-II|W-III)
COURSE DESCRIPTION (FOR AN EXISTING COURSE, DESCRIPTION EXACTLY AS IT CURRENTLY APPEARS IN THE SSU CATALOG; FOR A NEW COURSE,
DESCRIPTION EXACTLY AS PROPOSED ON THE NEW COURSE CURRICULUM FORM).
SAMPLE: This course introduces discipline X. It will provide foundational material and allow for future study in this discipline. Three
lecture hours per week. Excessive work outside of class will be required.
COURSE DESCRIPTION IS BEING CHANGED. Note: any changes to the existing course description must be directly
related to one or more aspects of certifying the course for one or more general education categories. Changes not related to
certification for general education categories require use of the Change in Course form.
REVISED COURSE DESCRIPTION EXACTLY AS IT IS TO APPEAR IN THE SSU CATALOG
NOTES (NOT FOR INCLUSION IN CATALOG):
SUBMITTED BY:
Department
(name of department chairperson)
(name of sponsor / contact person only if other than department chairperson )
department name Department
department website URL
Quantitative Reasoning Course Information Document
PREFIX nnn Course Title
n cr. QR
Catalog description:
Course description. Focus on the course goals in developing the description; do not incorporate implementation details unless
they are directly related to course goals. [n lecture hours [and m hours of scheduled laboratory per week] [plus work outside of class].]
Prerequisites: list all prerequisites (delete this line if there are no prerequisites)
Course Narrative:
Provide a reasonable (two to four paragraph) narrative that goes beyond the course description in articulating the pedagogical and
disciplinary mission and scope of the course. Think of this narrative as a means of connecting the dots between the course description
and course goals and outcomes.
Your narrative must include a separate description of how the criteria for certifying the course for the Quantitative Reasoning
general education category are addressed.
Course Goals:
(The wording of course goals may be phrased to meet disciplinary standards.)
This course will {introduce | explain | examine | …}:
G1:
G2:
G3:
general goal (not necessarily directly measureable);
general goal;
general goal; (insert additional goals as needed; try for no more than 3-5 goals)
Course Outcomes (Objectives):
(The wording of course outcomes may be phrased to meet disciplinary standards but outcomes must be assessable / measurable.)
Upon successful completion of the course, a student will be able to {demonstrate | explain | demonstrate | identify | …}:
O1:
O2:
O3:
specific (measurable!) objective (stated as something that the student did during the course);
specific (measurable!) objective;
specific (measurable!) objective; (insert additional objectives as needed; try for no more than 4-8 objectives)
Topics:
(Note: for special topics courses, provide appropriate separate topic outlines for two different special topics.)


topic one
 sub-topic

sub-sub-topic
topic two
 sub-topic

sub-sub-topic
Student Experiences: Describe the various type(s) of student experiences that will be used to assess student learning vis-à-vis stated
course objectives, e.g. presentations, tests, lab reports, writing projects, discussions, performances, etc. For each type, briefly describe
what the activity entails and provide an example or two of a typical assignment. Note that these examples are not meant to be prescriptive.
Student Experiences by Course Outcome (Objective) matrix:
(Eliminate or add columns and/or rows as necessary; each row represents a student experience (similar experiences should be grouped
if appropriate - this is NOT meant to be a list of every student experience); each column relates a student experience type to the relevant
course outcomes (objectives). Insert a check mark () in any cell where a given course objective is assessed via a specific student
experience.)
student outcome / experience
(e.g. presentations, tests, lab reports, writing projects,
discussions, performances, etc.)
O1
O2
O3
O4
O5
O6
QUANTITATIVE REASONING CRITERIA AND WHERE / HOW THEY ARE ADDRESSED IN THIS COURSE
CRITERIA FOR GENERAL EDUCATION
CATEGORY QR: QUANTITATIVE REASONING
QUANTITATIVE REASONING CRITERIA
CRITERION 1: LEARNING GOALS IN THE
REALM OF EMPIRICAL ANALYSIS
1A: COMMUNICATE DATA EFFECTIVELY
1B: COMPUTE USEFUL QUANTITIES FROM
DATA
1C: DRAW CONCLUSIONS USING DATA
1D: BUILD OR SELECT APPROPRIATE
MATHEMATICAL MODELS (THIS INCLUDES
UNDERSTANDING AND COMMUNICATING
THE ASSUMPTIONS INHERENT IN THOSE
MODELS)
CRITERION 2: LEARNING GOALS IN THE
REALM OF ABSTRACT MATHEMATICAL
MODELS:
2A: COMMUNICATE SUCH MODELS
CLEARLY, INCLUDING EXPLAINING WHAT
THEIR COMPONENT PIECES ARE
2B: USE SUCH MODELS TO MAKE
PREDICTIONS
2C: USE SUCH MODELS TO DRAW
CONCLUSIONS
2D: USE SUCH MODELS TO COMPUTE
USEFUL QUANTITIES
2E: FIND APPROPRIATE APPLICATIONS FOR
SUCH MODELS
DETAILS AND SUPPLEMENTARY INFORMATION REGARDING THIS GENERAL
EDUCATION CATEGORY AND ITS CRITERIA CAN BE FOUND AT
http://www.salemstate.edu/26049.php. NOTE THAT ALL CRITERIA MUST BE
ADDRESSED.
Course outcome(s) For each Quantitative Reasoning criterion, locate and identify
(objective(s)) that
CID elements aside from course outcomes, particularly
address a criteria
student experiences, which are directly related to that
(e.g. O3, O6)
criterion.
Sample Bibliography: (required for new courses only) (format as appropriate for your discipline; representative resources, not required
to be exhaustive)
(Note: for special topics courses, provide representative sample bibliographies for two different special topics.)
(examples)
Booch, Grady; Rumbaugh, James; Jacobson, Ivar. The Unified Modeling Language User Guide. Second Edition. AddisonWesley, 2005.
Gamma, Erich; Helm, Richard; Johnson, Ralph; Vlissides, John. Design Patterns: Elements of Reusable Object-Oriented
Software. Addison-Wesley, 1995.
Hwang, K.; Fox, G.; Dongarra, J. Distributed and Cloud Computing: From Parallel Processing to the Internet of Things.
Morgan Kaufmann, 2012.
(General Notes: anything in red must be deleted or replaced (and changed to black). Anything in red and in parentheses (like this note)
is an advisory comment and must be deleted. Note that while additional components may be inserted if appropriate, listed components
must be included. For examples, visit http://www.salemstate.edu/26049.php.)