LLU INSTITUTIONAL LEARNING OUTCOME: QUANTITATIVE REASONING RUBRIC
DEVELOPED FOR ACADEMIC AND PROFESSIONAL USE
Based on the AAC&U Quantitative Literacy VALUE Rubric, value@aacu.org, assessment@llu.edu, or see sites below
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Definition
Quantitative Literacy (QL) – also known as Numeracy or Quantitative Reasoning (QR) – is a "habit of mind," competency, and comfort in working with numerical data. Individuals with strong
QL skills possess the ability to reason and solve quantitative problems from a wide array of authentic contexts and everyday life situations. They understand and can create sophisticated arguments
supported by quantitative evidence and they can clearly communicate those arguments in a variety of formats (using words, tables, graphs, mathematical equations, etc., as appropriate).
Evaluators are encouraged to assign a zero to any work sample or collection of work that does not meet benchmark (cell one) level performance.
Interpretation
Ability to explain information
presented in mathematical forms (e.g.,
equations, graphs, diagrams, tables,
words)
Provides accurate explanations of
information presented in mathematical
forms. Makes appropriate inferences based
on that information. An example would be to
accurately explain the trend data shown in a graph
and make reasonable predictions regarding what
the data suggest about future events.
Provides accurate explanations of
information presented in mathematical
forms. An example would be to accurately
explain the trend data shown in a graph.
Provides somewhat accurate
explanations of information presented
in mathematical forms, but
occasionally makes minor errors
related to computations or units. An
example would be to accurately explain trend
data shown in a graph, but may miscalculate
the slope of the trend line.
Attempts to explain information
presented in mathematical forms, but
draws incorrect conclusions about what
the information means. An example would
be to attempt to explain the trend data shown in
a graph, but will frequently misinterpret the
nature of that trend, perhaps by confusing
positive and negative trends.
Representation
Ability to convert relevant information
into various mathematical forms (e.g.,
equations, graphs, diagrams, tables,
words)
Skillfully converts relevant information into
an insightful mathematical portrayal in a
way that contributes to a further or deeper
understanding.
Competently converts relevant
information into an appropriate and
desired mathematical portrayal.
Completes conversion of information
but resulting mathematical portrayal is
only partially appropriate or accurate.
Completes conversion of information
but resulting mathematical portrayal is
inappropriate or inaccurate.
Calculations attempted are essentially all
successful and sufficiently comprehensive
to solve the problem. Calculations are
presented elegantly, clearly, concisely.
Calculations attempted are essentially
all successful and sufficiently
comprehensive to solve the problem.
Calculations attempted are either
unsuccessful or
represent only a portion of the
calculations required to
comprehensively solve the problem.
Calculations are attempted but are both
unsuccessful and are not comprehensive.
Application / Analysis
Ability to make judgments and draw
appropriate conclusions based on the
quantitative analysis of data, while
recognizing the limits of this analysis
Uses the quantitative analysis of data as the
basis for deep and thoughtful judgments,
drawing insightful, carefully qualified
conclusions from this work.
Uses the quantitative analysis of data
as the basis for competent judgments,
drawing reasonable and appropriately
qualified conclusions from this work.
Uses the quantitative analysis of data
as the basis for judgments, lacking
inspiration or nuance leading to
marginal conclusions drawn from the
work.
Uses the quantitative analysis of data as
the basis for tentative judgments, and is
hesitant or uncertain about drawing
conclusions from this work.
Assumptions
Ability to make and evaluate
important assumptions in estimation,
modeling, and data analysis
Explicitly describes assumptions and
provides compelling rationale for why each
assumption is appropriate. Shows
awareness that confidence in final
conclusions is limited by the accuracy of
the assumptions.
Explicitly describes assumptions and
provides rationale for why assumptions
are appropriate. Shows awareness that
final conclusions are limited by the
accuracy of the assumptions.
Partially describes assumptions with
incomplete rationale..
Attempts to describe assumptions
without rationale..
Communication
Expressing quantitative evidence in
support of the argument or purpose of
the work (in terms of what evidence is
used and how it is formatted, presented,
and contextualized)
Uses quantitative information in
connection with the argument or purpose
of the work, presents it in an effective
format, and explains it with consistently
high quality.
Uses quantitative information in
connection with the argument or
purpose of the work. The data is
presented in an effective format.
Uses quantitative information, but
does not effectively connect it to the
argument or purpose of the work.
Presents an argument for which
quantitative evidence is pertinent, but
does not provide adequate support.
(May use quasi-quantitative words such
as "many," "few," "increasing," "small,"
and the like in place of actual quantities.)
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AAC&U - http://www.aacu.org/value/metarubrics.cfm; LLU Office of Educational Effectiveness - http://www.llu.edu/central/assessment/index.page