Definition of a Q course:
Courses appropriate for a Q designation should make continued use of mathematics or
statistics throughout the course at or above the basic algebra level. These courses might
include comprehensive analysis and interpretation of data. The mathematical and/or statistical
methods and skills required are those specific to the particular course and discipline.
Q Course Attributes:
- Courses should include mathematical or statistical descriptions of abstract, natural, or
social systems and phenomena. This may include, but is not limited to linear and
nonlinear functions, graphs, systems of equations, algorithms, formal abstract
structures, data analysis and interpretation. - Courses should require students to understand and carry out mathematical or
statistical manipulations to solve problems, make inferences, and draw conclusions.
Students should select appropriate tools and techniques to aid in their analyses, which
may include inputting and transforming quantitative information using a software
program or calculator and interpreting the output or results.
Quantitative Learning Objectives:
In line with the definition and criteria, the Q Working Group developed a set of Learning
Objectives for students across all Q courses. Specifically, we developed four Learning Objectives
required for all courses with the Q designation, and one recommended objective, depending on
a course’s content, discipline, or scope. This fifth Learning Objective, we acknowledge, is not
likely to be assessed across all disciplines, though we do encourage all instructors to seek ways
to incorporate an evaluative component when students are working with various sources of
quantitative information. We welcome feedback, specifically, on this item from the members of
the CCC+ committee at-large.
Q courses should meet all of the following learning objectives:
- LO1: Students should be able to identify a model or describe a dataset using
terminology appropriate for the course’s field of study. - LO2: Students should be able to develop models or set up problems using quantitative
tools and techniques appropriate for examining a given system or phenomenon. - LO3: Students should be able to accurately interpret quantitative information and
explain their reasoning. - LO4: Students should be able to perform manipulations and computational steps using
mathematical concepts and rules.
If applicable to the course method and content, Q courses should also meet the following
learning objective:
- LO5: Students should be able to evaluate the quality (e.g., identify strengths and
weaknesses) of sources of quantitative information.
Entry Expectations:
The present admission requirement for quantitative skills is the satisfactory completion of three or more years of high school mathematics course work including second-year algebra and first-year geometry. Students are strongly encouraged, however, to take four years of mathematics in high school. All students are expected to enter the University with a competency in basic algebra and quantitative reasoning as preparation for completing Q courses. All entering students will be evaluated for quantitative proficiency based on their Math SAT1 score and/or class rank.
Exit Expectations:
All students must pass two Q courses, which may also satisfy Content Area requirements. One Q course must be from Mathematics or Statistics. Students should discuss with their advisor how best to satisfy these requirements based on their background, prior course preparation and career aspirations. Students whose high school algebra needs strengthening should be encouraged to complete MATH 1011Q: Introductory College Algebra and Mathematical Modeling, as preparation for other Q courses. Alternatively, students may take MATH 1010: Basic Algebra With Applications (a course that does not carry credit toward graduation). To receive credit for Math 1011Q it must be taken before successful completion of another Q course. In some cases, advisors may recommend postponing registration in a Q course until after the student has completed a semester of course work at the University.
The University Quantitative Learning Center:
Advisors may also recommend that students avail themselves of support services offered at the University Quantitative Learning Center in Storrs and at the regional campuses. The Quantitative Learning Center will be directed by a full time faculty member who will oversee the administration of diagnostic examinations, quantitative-skills tutorials, workshops, modules, supplemental instruction, etc. The Quantitative Learning Center will also provide support to advisors and to faculty teaching Q courses on all campuses.